EXPECTATIONS OF AUTOMATICITY IN
BEGINNING INSTRUMENTAL MUSIC EDUCATORS
by
AMBER DAHLÉN PETERSON
Submitted in partial fulfillment of the requirements
For the degree of Doctor of Philosophy
Dissertation Adviser: Dr. Kathleen Horvath
Department of Music
CASE WESTERN RESERVE UNIVERSITY
May, 2012
AUTOMATICITY EXPECTATIONS ii
CASE WESTERN RESERVE UNIVERSITY
SCHOOL OF GRADUATE STUDIES
AUTOMATICITY EXPECTATIONS iii
Table of Contents
LIST OF TABLES……………………………………………………………….. vi
LIST OF FIGURES……………………………………………………………… viii
ACKNOWLEDGEMENTS……………………………………………………… ix
ABSTRACT……………………………………………………………………... x
FORWARD………………………………………………..…………………….. xii
CHAPTER
I. INTRODUCTION…………………………………………………………….. 1
Statement of the Problem…...………………………………………….... 5 The need for automaticity in memory…………………………... 11 Examples of automaticity………………………………………. 14 Automaticity research…………………………………………... 19 The need for automaticity in teaching…………………………... 21 Purpose of the Study…………………………………………………….. 24 Research Questions…………………………………………………….... 25 Definition of Terms……………………………………………………… 30 Delimitations…………………………………………………………….. 32
II. LITERATURE REVIEW…………………………………………………….. 35
Desired Results………………………………………………………….. 35 Expertise………………………………………………………… 35 Automaticity…………………………………………………….. 41 Characteristics and benefits of automatic skills………… 43 Limitations of automatic skills…………………………. 45 Choking…………………………………………. 46 Expert-induced amnesia………………………… 48 Adaptive expertise……………………………………………… 49 Expertise in teaching……………………………………………. 52 Experience………………………………………………. 56 Personal traits………………………………………….... 58 Knowledge of students………………………………….. 59 Schemata………………………………………………... 59 Teacher knowledge……………………………………... 61 Content knowledge……………………………... 62 Pedagogical knowledge……………………….... 66 Pedagogical content knowledge………………,... 67 Automaticity…………………………………………….. 69 AUTOMATICITY EXPECTATIONS iv
Problem-solving skills…………………………………... 72 Flexibility………………………………………………... 73 Recognition of patterns………………………………….. 74 The Beginning music educator………………………………….. 76 Knowledge and skills……………………………………. 77 Challenges……………………………………………….. 80 Summary of section……………………………………………... 82 Evidence…………………………………………………………………. 83 Knowledge and skill acquisition………………………………... 83 Psychomotor skills………………………………………. 84 Cognitive skills………………………………………….. 85 Bloom’s revised taxonomy……………………………… 89 Neurological basis of automaticity…………………………….... 90 Assessment………………………………………………………. 94 Summary of section……………………………………………... 102 Undergraduate Music Teacher Education……………………………….. 104 Curriculum………………………………………………………. 104 Specialists versus generalists……………………………………. 106 NASM requirements…………………………………………….. 109 Opportunities for deliberate practice……………………………. 110 Student teaching…………………………………………. 114 Field experiences………………………………………... 116 Criticisms………………………………………… 118 Service learning…………………………………………. 121 Effectiveness……………………………………………………... 124 Preparation potential……………………………………... 127 Recommendations for improvement…………………….. 127 Summary of section……………………………………………… 132 Summary of Chapter……………………………………………………... 133
III. METHODOLOGY…………………………………………………………... 136
Participants……………………………………...... 137 Data Collection Instrument……………………………………………… 143 Pilot Study……………………………………………………………….. 146 Data Analysis Procedures………………………………………………... 147 Validity and Reliability………………………………………………….. 150
IV. RESULTS……………………………………………………………………. 152
Music Teacher Education Preparation…………………………………… 152 Coursework………………………………………………………. 152 Field experiences………………………………………………… 156 Student teaching and certification………………………………... 158 Music Teacher Education Evaluation……………………………………. 160 Evaluation across programs……………………………………… 163 AUTOMATICITY EXPECTATIONS v
University supervision during student teaching…………………. 167 Automaticity Expectations of Music Educator Skills…………………… 168 Teaching skills…………………………………………………... 169 Performance skills………………………………………………. 173 Further analysis………………………………………………….. 178 Other categorizations……………………………………. 178 Correlations……………………………………………… 188 Summary of Results……………………………………………………... 193
V. DISCUSSION………………………………………………………………… 195
Summary of the Study…………………………………………………… 195 Findings………………………………………………………………….. 197 Music teacher education preparation……………………………. 197 Coursework……………………………………………… 197 Field experience.………………………………………… 200 Evaluation in music teacher education programs……………….. 201 Automaticity expectations………………………………………. 204 Correlations……………………………………………… 208 Further analyses…………………………………………. 211 Limitations of the Study………………………………………………… 211 Suggestions for Future Research………………………………………... 213 Conclusions and Implications…………………………………………… 215
APPENDICES…………………………………………………………………… 219
A. Music Educator Skills and Categorizations and Key……………. 219 B. Informed Consent Document and Survey of Automaticity Expectations……………………………..……………………….. 234 C. Email of Invitation for Study Participation………………………. 248 D. Reminder Email for Study Participation…………………………. 249 E. Final Reminder Email for Study Participation………………….... 250 F. Analysis for Possible Correlations between Program Attributes and Music Teacher Educator Expectations……………………… 251 G. Patterns of Teaching Skill Expectations Across Categories……... 253
REFERENCES…………………………………………………………………… 255
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LIST OF TABLES
Table
1.1 A Comparison of Phases of Skill Learning……………………... 10
1.2 Examples of Potentially Automatic Skills…………………….... 15
1.3 NASM Curricular Structure for Undergraduate Music Education Degrees………………………………………………. 26
2.1 Types of Automaticity in the Triple Mode View…………..…… 42
2.2 Characteristics of Expert Music Teachers………………………. 65
3.1 Other PK-12 Teaching Experience by Participants……………… 140
3.2 Frequency of Instrumental Music Teacher Educator Degrees…... 142
3.3 Research Questions in the Survey Instrument…………………... 149
4.1 Course Requirements for Instrumental Music Education Programs…………………………………………………………. 153
4.2 Technique Class Requirements for Instrumental Music Education Programs……………………………………………... 154
4.3 Laboratory Ensemble Requirements……………………………... 155
4.4 Undergraduate Involvement in Other Types of Field Experiences……………………………………………………… 158
4.5 Evaluative Tools used across Music Teacher Education Programs...... 164
4.6 Automaticity Expectations Scale………………………………... 170
4.7 Ranking of Automaticity Expectations for Teaching Skills…….. 171
4.8 Ranking of Automaticity Expectations for Performance Skills…. 174
4.9 Comparison of Expectations of Performance and Teaching Skill Categories………………………………………………….. 176
4.10 Significant Differences in Post Hoc Analyses of Automaticity Expectations…………………………………………………….. 177 AUTOMATICITY EXPECTATIONS vii
4.11 Ranking of Automaticity Expectations within NASM competencies……………………………………………………. 181
4.12 Significant Differences in Post Hoc Analyses of Automaticity Expectations within NASM competency rankings……………… 184
4.13 Significant Correlations between Coursework and Automaticity Expectations………………………………………. 190
4.14 Significant Correlations between Practice Activities and Automaticity Expectations………………………………………. 191
4.15 Significant Correlations between Evaluative Tools and Automaticity Expectations………………...………………...…… 192
A Music Educator Skills and Categorizations and Key…….………. 219
F Analysis for Possible Correlations between Program Attributes and Music Teacher Educator Expectations...... 251
G Patterns of Teaching Skill Expectations Across Categories…….. 253
AUTOMATICITY EXPECTATIONS viii
LIST OF FIGURES
Figure
i Conceptualization of the Word “Skill”…………………………... xvi
2.1 A Depiction of Shulman’s Types of Teacher Knowledge…..…… 62
2.2 A Classification System for Evaluation of Learning Outcomes… 97
4.1 Frequency of Courses with Field Experience Teaching Components……………………………………………………… 156
4.2 Frequency of Evaluative Tools used in Music Teacher Education………………………………………………………… 161
4.3 Frequency of Evaluative Tools used in Music Teacher Education………………………………………………………… 162
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Acknowledgements
This dissertation would have been impossible without the support of many friends, family, and teachers. First of all, I would like to recognize the expertise, support, and guidance from several Case Western Reserve University faculty including Dr. Kathleen Horvath, Dr. William Bauer, Dr. Matthew Garrett, and Dr.
Norah Feeny. All helped establish a strong foundation through many valuable courses and experiences. Thank you especially to my mentors in music education research –
Dr. William Bauer and Dr. Lisa Koops. I would also like to thank Kimberly Meier-
Sims at the Cleveland Institute of Music. It was while enrolled in her Suzuki pedagogy classes that I originally began contemplating the topic that would eventually become this dissertation.
Fellow Case Western Reserve University students were also invaluable, not only in their professional support but also in their personal friendship. I would like to especially thank my doctoral colleagues: Dr. Megan Clay Constantine, Dr. Vanessa
Bond, and Tammy Kuntz. It has certainly been an adventure.
This undertaking would also have been impossible without the support of my husband and parents. Thank you for always being there on this journey. Your encouragement made all the difference in the many ups and downs over the last several years. Thank you, Sean, for always being there for me, even when we were nine hours apart. I couldn’t have finished this without you.
AUTOMATICITY EXPECTATIONS x
Expectations of Automaticity in Beginning Instrumental Music Educators
Abstract
by
AMBER DAHLEN PETERSON
The purpose of this study was to examine instrumental music teacher educators’ perceptions regarding the level of automaticity of teaching knowledge and skill expected of beginning instrumental music teachers. The development of automaticity in some music educator skills may help beginning teachers avoid praxis shock, cognitive overload, burnout, and attrition. Instrumental music teacher educators
(n = 303) completed an online survey, in which data on their programs’ curricula, evaluation methods, and automaticity expectations were gathered. Expectations for teaching and performance skills were significantly different, with performance skills expected to be more automatic. Primary instrument performing and teaching, conducting, and performing on secondary instruments were highest rated at the approaching automatic level. Overall, the remaining skills were expected to be beginning automatic. None were expected to be completely automatic or nonautomatic. Correlations were weak or nonexistent between automaticity AUTOMATICITY EXPECTATIONS xi expectations and different characteristics and evaluations approaches of music teacher education programs. This study provided a picture of the current state of music teacher education, which might have implications for the development of beginning instrumental music teachers. Future research may further develop this new connection between automaticity and music teacher education.
Keywords: automaticity, instrumental music, teacher education, music education, curriculum, evaluation, skill, teaching, performance
AUTOMATICITY EXPECTATIONS xii
Forward
The term “skill” is used throughout this study. The author’s conceptualization of this word, as well as different varieties and functions, are outlined in the following paragraphs in order to clarify its meaning in the rest of the document. This concept is defined prior to the first chapter because of its core relevance for the entire study. At times, synonyms are also employed (e.g., ability, capability, competency, faculty, task, proficiency, capacity) while maintaining the original meaning.
For the purpose of this study, “skill” is defined as a psychomotor or cognitive task that has become proficient through a process of instruction, learning, and practice, but may also be influenced by other factors such as prior knowledge, experience, age, and ability (Ely & Rashkin, 2005). This term refers to abilities in general, unless accompanied by additional descriptors such as, but not limited to:
(a) music educator, (b) teaching, (c) performance, (d) cognitive, (e) psychomotor,
(f) affective, or (g) automatic. The generic word is retained in discussion of phases of skill learning, as well as much of the discussion of research on skill acquisition in the literature review, in order to coincide with existing research. “Skill” may be used as a generalization for any psychomotor or cognitive ability that is practiced, acquired, and demonstrated, or deemed necessary to achieve in order to be successful in a specific activity or field.
“Music educator skills” represent the set of competencies needed in order to function effectively as a music teacher. When this term is referred to in this general form, it signifies a more broad skill set, encompassing the many diverse skills that pertain to music educators. The music educator must often use many different skills AUTOMATICITY EXPECTATIONS xiii simultaneously. “Music educator skills” is an overarching term which subsumes the knowledge and execution of both teaching and performance skills. (These will be further defined in subsequent paragraphs.) Elliott (1992) made a similar distinction.
Music educator expertise required both musicianship and educatorship, the knowledge and competencies of both the musician and educator. The mere existence of “music educator skills” does not mean an individual will be a good teacher. They cannot guarantee success in the teaching environment. For instance, just because one might be able to demonstrate certain conducting techniques does not mean he is a
“good” conductor or can use such techniques in different contexts or concurrently with other necessary skills. However, this collection of “music educator skills” could possibly be used as a predicate to becoming a competent teacher.
Teaching is defined as the process of imparting to another the knowledge or abilities which are possessed by the teacher (Lexicon Publications, Inc., 1989). The term “teaching skills” denotes pedagogical and/or pedagogical content understanding that is demonstrated in music educators’ instructional abilities. Examples of this variety may include: (a) error detection, (b) error correction, (c) sequencing of instruction, (d) classroom management, (e) rehearsal abilities, and (f) use of technology. Some “teaching skills” have their basis in a shared pedagogical understanding among all educators (e.g., classroom management). Those skills that also rely on some content, or musical, knowledge, are more unique to the music educator. These skills, such as error detection, rely on both teaching abilities and musical knowledge, and are therefore, examples of pedagogical content knowledge. AUTOMATICITY EXPECTATIONS xiv
Many “teaching skills” are specific to the abilities of music educators, or may be developed to a higher extent when compared to other musical professions.
Performance is described as an activity that is physically represented and observable. Musically, it often refers to playing, singing, conducting, or related physical movements (Ely & Rashkin, 2005). The term “performance skills” is associated with physically demonstrated content knowledge (i.e., musical ability or comprehension). Further examples of “performance skills” in music educators include, but are not limited to: (a) application of music theory or history knowledge,
(b) playing primary and secondary instruments, (c) singing, (d) playing piano,
(e) repairing instruments, and (f) conducting. Many of these are not specific to music educators, but are shared by other musical professions. Depending on the preparation music educators receive, some “performance skills” may be less developed than their teaching abilities. Modeling performance technique or musical excerpts, an example of pedagogical content knowledge, involves an amalgamation of “performance” and
“teaching skills”.
“Teaching” and “performance skills” can also be considered in the cognitive, psychomotor, and affective learning domains. In the cognitive domain, comprehension is represented mentally. The term “cognitive skills” is synonymous with mental abilities. When Bloom’s revised taxonomy is alluded to in the study, the different categories of cognitive processes (i.e., remembering, understanding, applying, analyzing, evaluating, and creating) concern educational objectives in the cognitive domain. “Cognitive skills” are assumed to be involved in all “music educator skills” to some extent because of their association with the development in AUTOMATICITY EXPECTATIONS xv mental competencies. Examples include: (a) cognitive components which underscore physical performance, (b) interpretation of musical notation,
(c) consideration of theoretical and historical details in music, (d) management of extra-musical behavior in the rehearsal, (e) error detection and correction, and
(f) maintenance and adjustment of rehearsal plans, pacing, and routines.
The psychomotor domain refers to physical abilities and movements. “Music educator skills” that involve some type of physical movement correspond with
“psychomotor skills”, but are also in the cognitive domain because of the assumed mental abilities for the performance. Examples of this variety include:
(a) performance techniques for each instrument, (b) conducting gestures, and (c) using some technology and equipment.
“Affective skills” are related to the affective domain of learning, which concerns attitudes, emotions, interests, and values. This term is infrequently used in this study. “Cognitive” and “psychomotor skills” receive the most attention in
“teaching” and “performance skills”. However, “affective skills” are identified with some “music educator skills” in Table A, such as: (a) modeling performance technique or music, (b) classroom management skills, and (c) adjusting pacing or lesson plans to meet the needs of the learning context.
Automaticity is defined as the ability to perform a task without the need for conscious attention (Poldrack et al., 2005). “Automatic skills” refer to tasks in which one is able to perform without conscious attention, often in conjunction with other automatic or nonautomatic abilities. This term, as well as “automatic processes”, is derived from automaticity research, and could be assigned to a wide variety of well- AUTOMATICITY EXPECTATIONS xvi practiced skills, as seen in Table 1.2. This term can be used for any “music educator skill” which have been sufficiently learned and practiced and therefore can be performed with minimal conscious attention.
The author’s conceptualization and use of the word “skill” can be further illustrated in Figure i. This diagram shows the relationships and hierarchy between the varieties of skill that have been defined in the preceding paragraphs.
Figure i
Conceptualization of the Word “Skill”
Skill
Music Educator Skills
Teaching Skills Performance Skills
Cognitive Skills Automatic Psychomotor Skills Skills Affective Skills AUTOMATICITY EXPECTATIONS 1
CHAPTER 1
Introduction
Music educators require a discrete set of skills beyond those shared by other teachers. While all educators may utilize similar pedagogical understandings in general instruction, the content and pedagogical content knowledge (Shulman, 1986,
1987) of music teachers involves the pairing of cognitive and psychomotor abilities.
Similar dichotomies exist in the fields of physical education, dance, art, and technical education courses, but are not as evident in more academic subjects such as languages, math, and history, which primarily use the cognitive variety. In effect, music and comparable disciplines require the teacher to “perform” skills
(e.g., demonstrating technique on an instrument, conducting), as well as teach the subject matter. They must maintain the faculties of both musician and educator.
Webster’s Dictionary (Lexicon Publications, Inc., 1989) defines teaching as the process of imparting to another the capabilities, both mental and physical, which the teacher possesses. Music educators, therefore, must possess musical knowledge, and be able to musically perform, in order to convey this information to their students. To facilitate this connection, music teacher educators must pass on the necessary knowledge and competencies to pre-service teachers. The preparation of these future educators is not a simple undertaking when one considers the multi- faceted skill sets required by music educators, which fuse together teaching and performing abilities (see Appendix A). Furthermore, there is a difference between instructing pre-service teachers about music educator skills and actually guiding them through their execution. This distinction is seen when considering Bloom’s revised AUTOMATICITY EXPECTATIONS 2 taxonomy of educational objectives in the cognitive domain. (For this study, Bloom’s revised taxonomy refers to the revisions from Bloom’s (1956) original document edited by Anderson et al. in 2001.) Remembering, understanding, and applying are considered less advanced cognitive processes. More complex processes are achieved over time when the learner can analyze, evaluate, and create with the learned material
(Anderson et al., 2001). One of the focuses of this study concerns what instrumental music educator competencies can actually be demonstrated upon completion of an undergraduate music education degree, as well as which of these are learned to such an extent that they can be performed almost automatically.
Should beginning instrumental music teachers be able to teach or perform any cognitive or psychomotor tasks at an automatic level? Such a question concerns the psychological principal of automaticity, or the ability to execute well-learned skills with minimal conscious attention. If teachers are expected or required to be proficient in certain essential competencies when they enter the profession, are these focused on sufficiently in their undergraduate preparation? Wiggins and McTighe (2005) outlined three levels of content priorities, which were designed to organize and guide curriculum and instruction: (a) larger concepts and fundamental skills, (b) knowledge and abilities that were essential to develop, and (c) information students should be aware of but may not need to master at the time of instruction. With the diverse skill sets of instrumental music educators, what specific competencies fit into these three levels when pre-service teachers are being prepared?
Consider the role of the instrumental ensemble conductor. Psychomotor abilities must be balanced and performed in conjunction with their cognitive AUTOMATICITY EXPECTATIONS 3 counterparts. The unique performance techniques for each instrument in the band or orchestra, the intricate bi-manual coordination of conducting gestures, and the use of various technologies and equipment, are among only some of the psychomotor skills necessary for the music educator. These also have a cognitive component assumed to underscore their physical performance. Other more cognitive tasks required by the conductor include, but are not limited to: (a) the interpretation of musical notation;
(b) consideration of theoretical and historical details in relation to the music;
(c) management of extra-musical behavior in the context of the rehearsal;
(d) awareness of the classroom environment and potential distracters; (e) detection of errors in intonation, rhythm, or technique; (f) concurrent strategizing on methods for addressing mistakes or behavior (through verbal correction, movement, or physical modeling); and (g) maintenance and adjustment of rehearsal plans, pacing, and routines. When the preceding examples are physically represented, they could also be considered psychomotor.
Some music educator skills may be deemed more advanced due to their complexity, when considered in respect to the cognitive processes of Bloom’s revised taxonomy (Anderson et al., 2001). For example, if one reflects on the numerous underlying components of performing an instrument, the psychomotor abilities of holding the instrument, utilizing good posture, producing a competent tone, performing in tune, executing written notes with proper technique (e.g., fingering, embouchure, positions), and performing articulations, could be considered at the
“applying” level of the taxonomy. Other capabilities, such as justifying alternative fingering, demonstrating technique or musical examples for instructional purposes, AUTOMATICITY EXPECTATIONS 4 assessing and correcting student errors, and sequencing steps for instruction of beginners, subsume the preceding applied understandings and represent the more advanced levels of at analyzing, evaluating, and creating. Furthermore, one could consider the knowledge dimensions within the revised taxonomy. Music educators’ abilities to perform certain tasks signify knowledge of domain-specific skills, techniques, methods, or criteria for use, corresponding to the procedural knowledge dimension and transcending factual and conceptual knowledge. The ability to understand how students might struggle with certain topics or skills may be indicative of metacognitive knowledge, which further exceeds the procedural variety.
Music teaching and performance competencies, as well as the comprehension of many other topics ranging from psychology to philosophy, from curricular development to instruction of exceptional learners, are the responsibility of undergraduate music teacher education programs. Not only are these programs required to offer instruction on all the preceding psychomotor and cognitive skills and pedagogical knowledge, part of the college degree must also include field experiences in which these abilities can be practiced and further developed. The overall curriculum of music teacher education is group taught between many faculty members with various specializations, expertise, and responsibilities. Therefore, individual professors have only proprietary control of certain aspects of the curriculum. Furthermore, those aspects in which professors have some power may also have state, federal, or professional stakeholders with their own mandates.
Examples of these are outlined in such professional documents as the Interstate New
Teacher Assessment and Support Consortium (InTASC) standards (Council of Chief AUTOMATICITY EXPECTATIONS 5
State Officers [CCSO], 2010), the National Council for Accreditation of Teacher
Education (NCATE) standards (NCATE, 2008), and the National Association of
Schools of Music (NASM) competencies (NASM, 2010).
This study seeks to examine the preparation of beginning instrumental music educators through the expectations of music teacher education programs and faculty.
There is currently no research directly connecting automaticity and music teacher education, although there have been similar efforts in other fields. It is the intent of the researcher to address this gap in music teacher education research.
Statement of the Problem
Beginning teachers frequently experience praxis shock, which is the disparity between expectations and the encountered realities of the profession (Mark, 1998).
Praxis shock is also referred to in related literature as practice, reality, or transition shock, or unrealistic optimism. Another author described it as the danger beginning teachers feel as their ideals are threatened by the realistic challenges of the education profession (Veenman, 1984). Marso and Pigge (1987) characterized praxis shock as the large amount of problems experienced in the first year of teaching resulting in observed differences in teaching behavior, attitude, and personality.
Ballantyne (2007a; 2007b) interviewed beginning teachers to determine their perceptions of both their career and their preparedness to enter the music education profession. Responses suggested praxis shock was a common experience for most young music educators. Most did not feel prepared for the realities of teaching after completing their undergraduate education. Many beginning teachers left during or at the end of their first year of teaching as a result of praxis shock (Marso & Pigge, AUTOMATICITY EXPECTATIONS 6
1987). Forvilly (2004) included this imbalance between idealism and realism as one causal factor of teacher burnout. This is cause for concern, considering music educators burned out significantly more frequently than general classroom teachers, with orchestra teachers showing the highest rates (Hamann, 1986b). Beginning music educators with fewer than six years teaching experience were also more likely to burnout (Hamann, 1986a).
Many potential causes of praxis shock have been proposed. A frequently heard reason involved inadequate pre-service teacher preparation. Mark (1998) suggested praxis shock might be a result of an insufficient emphasis on the development of actual teaching skills. Deficiencies in teacher preparation were also among several explanations given by others (Müller-Fohrbrodt, Cloetta, and Dann,
1978 as cited in Veenman, 1984; Stokking, Leenders, De Jong, & Van Tartwijk,
2003). Richards and Killen (1993) specifically criticized music teacher education programs’ failure to provide enough teaching opportunities within realistic situations.
In Veenman’s (1984) review of studies from 1960 to the early 1980s, many researchers criticized teacher education programs for beginning teachers’ problems.
However, the researcher believed this was only justified in regard to: (a) the overemphasis on subject matter knowledge rather than pedagogical skills, (b) the presentation of information in isolation, (c) the limited student teaching period, and
(d) poor institutional control over how knowledge and skills were developed in the school contexts. He also thought criticism focused exclusively on teacher education was unjustified because of the impossibility for these programs to anticipate all potential difficulties beginning teachers might experience. AUTOMATICITY EXPECTATIONS 7
Personal issues might also contribute to praxis shock in beginning teachers.
Weinstein (1989) attributed its occurrence to pre-service teachers’ overconfidence, which might have caused them to filter out information during their undergraduate education that they viewed as irrelevant. In a study on the expectations of pre-service music teachers concerning the problems of beginning teachers, undergraduate students showed signs of unrealistic optimism, consistently expressing much confidence that they would experience fewer problems than the average beginning teacher (Richards & Killen, 1993). This replicated Weinstein’s (1988) research, in which the term “unrealistic optimism” signified the belief that problems experienced by others would not happen to the optimistic individual. Many pre-service teachers
(68%), regardless of their academic year, thought they would be “fairly good” or
“good” music educators without any further music teacher education. When provided with examples of difficulties frequently faced by beginning teachers, most pre-service teachers believed they would only be moderately troubled by these in their own first year (Richards & Killen, 1993). Other potential causes of praxis shock included
(a) general false expectations (Stokking et al., 2003), (b) a wrong career choice, and
(c) attitudes and personality characteristics not conducive to success in teaching.
Professional issues related to beginning teachers’ position might also cause praxis shock. One factor could be the excessive amount of responsibilities and demands of the occupation (Müller-Fohrbrodt et al., 1978 as cited in Veenman, 1984;
Stokking et al., 2003; Ballantyne, 2007b) or the isolation often experienced by beginning teachers (Müller-Fohrbrodt et al., 1978 as cited in Veenman, 1984;
Ballantyne, 2007b). Additionally, problems within the school setting, such as AUTOMATICITY EXPECTATIONS 8 relationships with other colleagues or administrators, teacher shortages, a lack of necessary materials, and no clear instructional objectives could contribute to praxis shock.
Marso and Pigge (1987) measured reality shock of beginning teachers by comparing their pre-service expectations to what they actually experienced early in their professional careers. Differences appeared in relation to school location
(i.e., rural, urban, and suburban), teaching level, and subject. For example, secondary- level and urban teachers reported the most praxis shock. All beginning teachers conveyed praxis shock to some extent despite the fact that they had completed lengthy clinical and field experience mandates during their pre-service preparation.
The only work conditions that surpassed expectations were assistance from teaching colleagues and administrative observations.
It is hypothesized in this study that beginning teachers’ acquisition of some automatic skills during their preparation may provide these educators with a collection of accessible, well-practiced teaching and performance skills, which could potentially help in the avoidance of praxis shock, cognitive overload, burnout, and professional attrition. This is similar to a proposal by Stokking et al. (2003): Praxis shock could potentially be avoided by providing more realistic, extended, and intense practice opportunities in the music teacher educator curriculum. While it is impossible for undergraduate programs to prepare all essential music educator competencies to an automatic level in four to five years, certain fundamental skills could be developed and practiced in such a way to allow teachers to be beginning or approaching levels of automaticity by the time they enter their first year of teaching. AUTOMATICITY EXPECTATIONS 9
The researcher’s consideration of automaticity in the development of undergraduate pre-service teachers originated with Shinichi Suzuki’s phases of skill learning in musical performance. Suzuki’s (1983) ideas were more philosophical than scientific, based on many years of observing and teaching violin to children. As part of the “Mother Tongue” approach, these phases were directed toward how children developed linguistically and musically. In the first stage, the learner understood what must be done. The second stage involved meaningful practice by the learner, in addition to appropriate feedback from an instructor. After an adequate amount of practice, the student might attain the third stage, in which the skill could be performed automatically (Starr & Starr, 1983). This last stage is similar to the psychological principle of automaticity. Upon sufficient learning and practice, the learner will reach a point at which an ability can be performed with little conscious attention, thereby allowing attention to be directed elsewhere (Poldrack et al., 2005). Almost all cognitive and psychomotor skills can be performed faster and more accurately with practice, eventually achieving a level in which they become automatic, habitual, or routine (Ashby, Tuner, & Horvitz, 2010).
It would be inappropriate to build a study upon phases of skill learning that were not initiated in scientific research. Fortunately, there is a parallel in psychology.
Around the same time Suzuki was developing his phases, Fitts was studying how skills, in general, were acquired from a psychological perspective (Fitts, 1964; Fitts &
Posner, 1967). Any connection between these figures is purely theoretical. However, it is possible Suzuki was aware of this concurrent research. Table 1.1 compares the three phases of both individuals. AUTOMATICITY EXPECTATIONS 10
Table 1.1
A Comparison of Phases of Skill Learning
Fitts Suzuki (Research-based) (Philosophy-based) 1. Early or Cognitive Phase: The 1. The learner understands what needs individual attempts to understand the to be done. skill. Cues, events, and responses that will be overlooked in the future must be attended to in this stage. The learner finds instructions and demonstrations most helpful.
2. Intermediate or Associative Phase: 2. The learner engages in meaningful Skills that were introduced in the practice with appropriate feedback. previous phase are now practiced, allowing for new patterns to appear. Mistakes are frequent at the beginning of this phase, but gradually disappear with practice. The length of time the learner remains in this phase depends on the complexity and novelty of the task.
3. Final or Autonomous Phase: 3. The learner can execute the skill Components gradually become more automatically. autonomous, requiring less cognitive control. Simultaneous tasks and distractions impede the performance less. The learner can engage in new learning or other activities during execution. Speed and efficiency continue to improve. These highly practiced skills become similar to reflexes because they can occur without much verbalization or focused attention. In fact, after this phase has been achieved, concentration on the task can often impede performance. Learning continues to take place, as faster skills replace their slower counterparts. Note. From Fitts, P.M. (1964). Perceptual-motor skill learning. In A.W. Melton (Ed.), Categories of human learning (pp. 243-285). New York: Academic Press; Fitts, P.M. & Posner, M. I. (1967) Human performance. Belmont, CA: Brooks/Cole Publishing Company; and Starr, W. & Starr, C. (1983). To learn with love: A companion for Suzuki parents. Van Nuys, CA: Alfred Publishing Co., Inc.
AUTOMATICITY EXPECTATIONS 11
These phases can be applied to undergraduate pre-service music teacher education curriculum and how individuals learn to become music teachers. During music education coursework, pre-service teachers are often working in the first learning phase. They are studying about the skills and knowledge required of music educators. The second phase is likely entered into during field experiences or the student teaching period, in which music educator competencies can be practiced.
Whether beginning teachers reach the third, automatic phase in any ability while still in their undergraduate program, or if they remain in the second phase during their initial experience as professional educators, is dependant on the preparation and practice opportunities afforded by their music teacher education programs. Programs often do not combine all performance and teaching skills of music educators until the student teaching practicum. The development of automaticity in certain aspects of music teaching may be a valuable way to consider the effectiveness of pre-service music teacher preparation. At present, there is no instrument or process to quantify automatic teaching and performance skills of beginning music educators, to assess where pre-service teachers are in the process of developing automaticity, or to determine deficiencies for entering undergraduates, which could further inform remediation plans to assist in automaticity achievement.
The need for automaticity in memory. Although potential evidence of automaticity is seen in the execution of skills, it could also be viewed as a mechanism for increasing memory capacity. Memory is defined as the ability to recall previously acquired information through the process of storage, encoding, and retrieval. When viewed through the dual-store model, information is collected temporarily in the AUTOMATICITY EXPECTATIONS 12 sensory registry upon initial perception. Processing is moved to the working memory if the individual is consciously attentive to the information. However, the capacity of the working memory is very limited, as is the amount of information to which one can attend. Storage is only temporary (Ormrod, 2008). Also, working memory is vulnerable to distractions and memory loss (Zull, 2011). If information is to be retained for future recall, it must transition into the long-term memory. This area is believed to be able to store an infinite amount of information for an unlimited amount of time. When new learning must be connected to previously acquired knowledge, the encoded memory is retrieved from the long-term memory back into the working memory. The ultimate key to memory storage, encoding, and retrieval is attention
(Ormrod, 2008; Zull, 2011).
When processes occur consciously, they occupy a larger portion of the working memory. Thus, attention is less available for other cognitive activities
(Feldon, 2007a). The amount of information that can be perceived at any moment is so large that it is impossible to attend to all of it consciously. Incoming information must be filtered, resulting in a limited amount being processed at any given moment
(Syed, 2010). Over a century ago, the psychologist William James (1890) questioned how many ideas could be attended to at any moment. At that time, it was believed the capacity was limited to a single idea. However, attention could be extended to two or three ideas if some tasks were habitual. For processes requiring much effort, focused attention on the single idea caused much interference to the perception of other processes. It is currently believed that approximately seven pieces of information can be processed at any single moment. Chunking multiple bits of information into larger AUTOMATICITY EXPECTATIONS 13 units can expand attention capacity (Csikszentmihalyi, 1990; Ormrod, 2008).
If achievement of automaticity allows a task to be performed with little conscious attention, less of the limited capacity of attention and working memory may be occupied (Csikszentmihalyi, 1990; Poldrack et al., 2005). Temporary storage capacity and automatic processing were the focuses of a study on the comprehension of adult deaf readers. Kelly (2003) defined the former as the ability to hold information in working memory, while automatic processing involved the capacity to perform basic reading tasks with minimal intentional effort. Both were believed to interact with each other in working memory, as well as share its capacity. Results showed less proficient readers had low automaticity, which might have caused more working memory capacity to be utilized, as well as a reduction in the readers’ ability to temporarily hold information during processing. Another potential problem associated with low automaticity was slower processing time resulting in faster decay of temporarily stored information.
The instance theory of automatization represented an attempt to connect automaticity to memory and attention. Processes were considered automatic when necessary information could be retrieved from long-term memory in a single-step.
This theory signified an alternative view of automaticity, explaining novice performance as a lack of knowledge, rather than limited memory capacity. The author believed encoding and retrieval of memories were inevitable outcomes of attention.
Additionally, each experience went through the memory process (encoding, storing, and retrieving) individually. The learner eventually stored sufficient episodic memories to transition from algorithmic (step-by-step) processing to memory-based AUTOMATICITY EXPECTATIONS 14
(single-step) processing (Logan, 1988).
In a related study, Strayer and Kramer (1990) examined whether achieving automaticity was associated with the ability to directly access related previous solutions from the long-term memory. The development of automaticity was attributed to reduced dependence on information stored in the working memory.
When working memory was relied upon, processing was considered more controlled and less automatic. Automaticity was suggested when information could be more quickly retrieved from the long-term memory.
James (1890) also discussed how automatic skills evolved from multiple step memories to more efficient, single step recall:
In all acquired dexterities and habits, secondarily automatic performances as
they are called, we do what originally required a chain of deliberately
conscious perceptions and volitions As the actions still keep their intelligent
character, intelligence must still preside over their execution. But since our
consciousness seems all the while engaged, such intelligence must consist of
unconscious perceptions, inferences, and volitions (p. 165).
Examples of automaticity. Tasks that are performed automatically are rarely thought about: Individuals are often unaware of the intricacies of their actions. There are numerous examples of automaticity in daily activities, music, and teaching. They usually earn this description as a result of observation of the performance, but the underlying processes are also assumed to be automatic (Moors & Houwer, 2007).
Coyle (2009) theorized that automaticity developed as an evolutionary adaptation. If more tasks could be executed with little concentration, more attention could be AUTOMATICITY EXPECTATIONS 15 directed toward potential dangers in the environment. An extensive list of potentially automatic skills was organized by Bloom (1986). Table 1.2 provides some examples of each category from his article.
Table 1.2
Examples of Potentially Automatic Skills
Categories Skills Bodily control Eating, walking, running Household Sewing, using utensils, sawing Communication Speaking, reading, writing Man-instrument Playing musical instruments, singing, dancing (trained) Man-machine Driving, flying, cycling Sports Swimming, diving, jumping Note. From Bloom, B.S. (1986). “The hands and feet of genius”: Automaticity. Educational Leadership, 43(5), 70-77.
All preceding examples listed could be assessed through observation. Bloom
(1986) recognized that many cognitive skills in technical and professional fields could become automatic by those who extensively used them. However, at the time of his article, these abilities had not been as extensively explored as the more obvious tasks. More recent research has studied automaticity in higher-order skills such as understanding, assessing, and making decisions in order to improve military personnel performance (Holt & Rainey, 2002). Research in social psychology has also recently examined automaticity in perceptions, stereotyping, judgment, and behavior (Bargh, 1997). For example, perceptions, in the form of initial impressions, represent a type of pre-conscious automaticity. Certain behaviors or traits that are repeatedly presented may eventually lead to the development of this type of automaticity, resulting in the formation of impressions without conscious attention to the cause of these beliefs. Holt and Rainey (2002) concluded in their review of literature that more complex, higher-order tasks appeared to be a combination of AUTOMATICITY EXPECTATIONS 16 controlled and automatic processes. This presented an issue in the training of such tasks, as one had to determine which processes could be automatized.
There has been little research on automaticity in music. Lonis (1993) theorized concepts of automaticity had unknowingly been incorporated into music teacher education in how conducting competencies were developed. Automatization appeared in Mishra’s (2005) study on musical memorization as part of the final, over- learning stage in the memory process: She defined automatization as the consistent repetition of a sequenced activity over a lengthy period, resulting in a routine, possibly inflexible, performance that could be executed unconsciously. Examples of automatic skills supplied by the author included physical, performance abilities and retrieval cues. Automaticity could enable the musician to direct their attention to other aspects of the performance beyond the actual execution of technique.
Anderson’s (2005) literature review on research of cognitive, biological, and musical abilities in infants made a distinction between biofunctional and structural automaticity in an attempt to explain infant reflexes. Structural automaticity represented the variety used throughout this study, but was not applicable to the abilities of infants due to its reliance on deliberate practice. Rather, biofunctional automaticity was a result of the natural functioning of the body and brain. Both types exhibited a lack of conscious effort, but biofunctional automaticity did not involve intentional learning. Wan and Huon’s (2005) study of attention’s detrimental effect on piano performance was related to the psychological studies on choking, a potential limitation of automaticity studied more so in psychology. The concept of automatism, as a requirement for the skilled, yet musical, movements of musicians, was discussed AUTOMATICITY EXPECTATIONS 17 in Wilson’s (1989) article. By developing automaticity in the mechanics of a performance, the musician would be able to delegate more attention to other aspects.
Automaticity has been infrequently connected to teaching skills. When sources on expert, accomplished, or effective teaching were reviewed for this study, very few articles specifically included automaticity among characteristics of these educators. Berliner (2004) proposed that expert teachers displayed the capacity to automate professional routines or repetitive tasks. In fact, some of the earliest research on pedagogical expertise found experts’ abilities to “think on their feet” was the result of automatization of routines. These routines were often so automatic that they were practically unconscious (Berliner, 1986). Hammerness et al. (2005) defined teacher expertise as the ability to perform a variety of activities without having to think about how to perform them, an idea very similar to the principle of automaticity. Specific activities were mentioned, such as how to manage a class that is doing group work, how to give directions, and how to hand out material while maintaining student attention.
Csikszentmihalyi’s (1990) concept of optimal experience, or flow, is reminiscent of automaticity. Flow was defined as psychic energy, which proceeds with minimal effort when information entering consciousness matches the goals of the individual. This state allows attention to be freely focused on other needs. It was also theorized that upon the achievement of flow, routine processes of work would no longer be boring, but rather purposeful and enjoyable. In a more recent chapter by
Csikszentmihalyi and Nakura (2010), automaticity was theorized to be an important part of flow experiences because of its ability to make attention more available to AUTOMATICITY EXPECTATIONS 18 other tasks. The authors gave the example of a surgeon who had developed many automatic routines. The resulting increase in attention capacity allowed him to be consciously aware of other things beyond the well-learned, effortless tasks. The authors emphasized that effortless attention did not signify less attention was being put forth, but rather more information could be processed. Relating back to the balance of flow, attention is most focused when environmental challenges that demand attention are balanced with the competency of the individual. If challenges outweigh skill (as in some situations confronted by beginning teachers), much effort and attention is required. However, if an individual’s competence surpasses the necessities of the situation, the individual can become distracted because so little attention is needed. Flow equals balance.
The Taoist idea of Yu was also discussed in Csikzsentmihalyi’s (1990) work because of its semblance to flow. It also shows striking similarities to automaticity.
This concept appeared in a parable over two thousand years ago, concerning a servant who was complimented by his master for his dance-like, automatic performance while butchering an ox. According to Taoism, Yu occurs when the individual no longer relies on conscious mastery, executing the task with effortless, unconscious ease. Through focus of attention and practice, an automatic performance that appears
“spontaneous or otherwordly” is achieved (p.151).
Gladwell (2005) defined a similar concept, rapid cognition, as the need to quickly make decisions in stressful situations based on one’s prior knowledge. An important feature of rapid cognition is “thin-slicing,” which is the ability to subconsciously detect patterns in situations and behavior as a result of very narrow AUTOMATICITY EXPECTATIONS 19 bits of experience. He further characterized “thin-slicing” as automated and unconscious. Gladwell’s (2005, 2008) writings are among several recent popular books discussing current interests in psychological, neurological, and educational topics, including those related to automaticity such as attention, expertise, intuition, and choking (Beilock, 2010; Buonomano, 2011; Chabris & Simons, 2010; Coyle,
2009; Eagleman, 2011; Marcus, 2012; Sousa, 2010; Strauch, 2010; Syed, 2010; Zull,
2002; Zull, 2011).
Automaticity research. Automaticity has been the focus of extensive cognitive science research over the past forty years (Poldrack, et al., 2005). In the last twenty-five years, automaticity researchers in psychology have concentrated on higher mental processes, such as judgment, motivation, and social behavior (Bargh,
2007). Despite this recent attention in research, the concept of automaticity is not new. It appeared as early as 1890 in psychological writings, promoted as a potential universal that all humans could perform a task effortlessly if it had been well learned
(James, 1890).
There are two purposes behind automaticity research: to identify the performance of a task as automatic or nonautomatic, and to explain the general phenomenon of automaticity. The former is referred to as the feature-based approach.
In earlier studies, performances were initially labeled automatic if they appeared unintentional, unconscious, uncontrollable, and efficient, according to the dual mode view of automaticity. The lack of any of these would relegate the process to the nonautomatic mode. A more modern version of this approach, the triple mode view, labeled tasks as automatic or nonautomatic, but further divided automaticity into AUTOMATICITY EXPECTATIONS 20 preconscious, postconscious, and goal-dependent varieties (Bargh, 1989, 1992; Moors
& Houwer, 2007). Other researchers prefer to use the mechanism-based approach, which examines more at how skills become automatic (Moors & Houwer, 2007) and relates more to the previous section on the connection between automaticity and memory.
Research studies (Beilock, Carr, McMahon, & Starkes, 2002; Beilock,
Wieranga, & Carr, 2002; Brown & Carr, 1989; Poldrack et al., 2005; Schneider &
Shiffrin, 1977) frequently use dual-task performances to identify automatic processing. This involves performing a practiced ability while also attending to another task. If a subject is able to perform the primary task in the presence of the secondary distraction, researchers may assume the initial skill has achieved some level of automaticity. However, if the additional task interferes and disrupts the original, it may be nonautomatic. This variety often compares performances between groups of subjects, such as experts and novices. Other studies (Debaere, Wenderoth,
Sunaert, Van Hecke, & Swinnen, 2004; Hocking, 1999; Poldrack et al., 2005;
Puttemans, Wenderoth, & Swinnen, 2005) examined efficiency in neural activity as potential evidence of automaticity. More brain activity often appears when a task is first introduced and learned. With practice, activity is reduced as the skill and neurological functioning become more efficient. The limitation of choking has also been the focus of some automaticity research (Baumeister, 1984; Beilock & Carr,
2001; Masters, 1992; Wan & Huon, 2005). This occurs when performance is disrupted due to the individual’s increased attention. Conscious control may be AUTOMATICITY EXPECTATIONS 21 detrimental if execution is usually successful when performed implicitly. Findings of these preceding studies are further clarified in the literature review.
Automaticity has been examined in many disciplines beyond cognitive science and psychology. Educational research has considered automaticity in terms of special learners, literacy, language acquisition, and the development of mathematical skills. It was also viewed as one of the goals of education in terms of acquiring calculation skills (Dehaene, 2010). These sources generally concentrated on automaticity in students, rather than the teacher. Neuroscience has also examined some of the brain activity associated with automaticity. Sports research has looked at automaticity in essential physical capacities. Related to the focus of this study, there were numerous studies on the development of automaticity in occupational training (e.g., security screening, medical professions, military duties, aviation, counseling, and ethics teaching). The few studies that addressed automaticity in the development of teachers are discussed in the literature review.
The need for automaticity in teaching. The idea that automaticity is necessary for effective teaching is not a new one, although reference to this psychological principle was not always explicit. For example, John Dewey stated in
1933:
The teacher must have his mind free to observe the mental responses and
movements of the student members of the recitation-group. The problem of
the pupils is found in subject matter; the problem of teachers is what the
minds of pupils are doing with this subject matter. Unless the teacher’s mind
has mastered the subject matter in advance, unless it is thoroughly at home in AUTOMATICITY EXPECTATIONS 22
it, using it unconsciously without the need of express thought, he will not be
free to give full time and attention to observation and interpretation of the
pupils’ intellectual reactions. (p. 274)
The statement reflects the need for the teacher’s attention to be available for focus on his students. Additionally, Dewey mentioned prior mastery of the subject to the extent that much of it could be performed unconsciously, which was very similar to the concept of automaticity.
The complexity and cognitive demand of teaching first began to be widely realized in cognitive psychology and educational research of the 1970s (Westerman,
1991). However, little continues to be known about how individuals begin to think and perform like teachers (Austin & Miksza, 2012). Comprehension continues to advance, especially through the emerging field of teaching expertise (Berliner, 2004).
When teachers perform in the classroom context, there are many things to be conscious of simultaneously in order to manage the learning environment effectively, including the many dimensions of student understanding and behavior and the rapid pace of activities and experiences (Doyle, 1986). If some teaching skills can be automatically executed, the capacity of the working memory, as well as the teacher’s attention, could possibly be directed toward the needs of students, environmental factors in the classroom, nonautomatic teaching abilities, and teachable moments.
Through a review of research on teaching and teacher preparation, Feldon
(2007a) sought to show the usefulness of a cognitive theoretical framework based on the dual-store model of memory. When consciously involved in certain tasks, a portion of the working memory is occupied, making it unavailable for other AUTOMATICITY EXPECTATIONS 23 processes. Once knowledge becomes automatic, less working memory is necessary.
Subsequently, these tasks require little or no cognitive load for the practitioner.
Feldon concluded that the development of automaticity in some teaching skills might be beneficial, if not critical, to teacher success because of its ability to reduce the overall cognitive load needed to function in the classroom. Likewise, Syed (2010) pointed out that if attention is overloaded, a person’s capability in perceiving things that were actually present might be impeded. Individuals generally have the same capacity for attentive, conscious processing. However, experts are able to seemingly increase their capacity by automatizing certain processes, thereby removing them from consciousness.
Beyond the reduction of cognitive load, Feldon (2007a) believed development of automaticity in teacher preparation could improve teacher performance by increasing the likelihood teachers would utilize adaptive behaviors if cognitive overload was reached. It could also increase the amount of working memory available for consciously monitoring evaluative activities in an effort to avoid biases. If more cognitive capacity was available, teachers would be able to allocate more attention to the needs of their students. Furthermore, the development of automaticity might enable cognitive capacity to be reinvested back into processes, which would help to further develop teaching skills to higher levels. Bereiter and Scardemalia (1993) claimed this concept of reinvestment was essential if normal learning was to transcend to the acquisition of expertise. They identified three ways in which reinvestment could contribute to expertise: (a) reinvestment into further learning; AUTOMATICITY EXPECTATIONS 24
(b) exploration for more complex problems that did not necessarily require individuals to work harder, but rather to use new knowledge, develop new abilities, or use previous skills in novel ways; and (c) the development of a more thorough understanding of the complexities of frequently occurring problems.
When considering teacher preparation in respect to automaticity, pre-service teachers could be supplied with additional practice in routines typically used by teachers. While theoretical knowledge was important, Berliner (1988) believed novices would benefit more from practicing these activities because, once they were in front of their own class, they would be able to quickly learn other recurring tasks.
Such routines are valuable because they allowed the expert teacher to go into an automatic mode, making it possible for the teacher to process other information and concentrate on different aspects of their teaching. As has been seen in the expert domains of professional musicians, athletes, and chess masters, routinizing entire sequences within a task may allow the expert to think ahead to subsequent steps or monitor subtleties in their performances. Berliner recommended the identification and emphasis on teaching routines in teacher education.
Purpose of the Study
The purpose of this study was to examine instrumental music teacher educators’ perceptions regarding the level of automaticity of teaching knowledge and skill expected of beginning instrumental music teachers.
AUTOMATICITY EXPECTATIONS 25
Research Questions
1.) How were music teacher education programs preparing the music educator
skill set necessary for beginning instrumental music teachers in their current
undergraduate students?
2.) How were music teacher education programs evaluating the music educator
skill set necessary for beginning instrumental music teachers in their current
undergraduate students?
Undergraduate teacher education is the first formal encounter pre-service teachers have with how teachers think and perform. They enter these professional programs with much prior knowledge about what teachers do following many years of observing teachers from a student viewpoint. (Feiman-Nemser & Buchanan, 1986).
Haston and Leon-Guerrero (2008) theorized that instrumental music education students had more hours of pre-collegiate observation than pre-service teachers in other subjects because most had been playing in ensembles and/or taking private lessons for at least seven or eight years prior to entering the music education program.
Conversely, pre-service teachers’ previous perspectives differed from the pedagogical view they would go on to develop in their teacher preparation coursework (Feiman-
Nemser & Buchanan, 1986).
While it is assumed some performance skills used by instrumental music educators are acquired beforehand, this study focuses on the preparation received in college. Schmidt (1989) attempted to find evidence of an instructional core in music teacher education programs. However, Cutietta (2007) questioned, “how ‘core’ is this core curriculum to the knowledge set [music teacher educators] are trying to develop” AUTOMATICITY EXPECTATIONS 26
(p.13). Did these classes represent the basis of professional knowledge necessary for educators, or did they merely pass on generic information? Professional organizations and accrediting agencies, such as NASM and InTASC, have established criteria for music teacher education programs, which likely influence what is offered to undergraduate pre-service teachers. For example, NASM provides the following curricular structure for undergraduate degrees in music education (i.e., Bachelor of
Music Education, Bachelor of Music in Music Education, Bachelor of Science in
Music Education, and Bachelor of Arts in Music Education) (see Table 1.3). There is, however, still much potential variability in music teacher education programs because
NASM only provides percentages for different categories, rather than a prescriptive formula. Programs and curricula might reflect the particular biases, philosophies, requirements, strengths, and weaknesses of their faculty, department, or university.
Table 1.3
NASM Curricular Structure for Undergraduate Music Education Degrees
Percentage Curricular Area Examples of Courses 50% Musical Studies Applied Lessons, Ensembles, Music History, Music Theory, Piano, Conducting, Music Education Methods Courses, Secondary Instrument Technique Classes
30-35% General Studies Math, Sciences, English, Foreign Languages, Social Studies, Physical Education
15-20% Professional Education Philosophical and Social Foundations of Education, Educational Psychology, Special Education, History of Education, Student Teaching Note. From The National Association of Schools of Music. (2010). The National Association of Schools of Music Handbook. Reston, VA: National Association of Schools of Music.
The survey instrument for this study specifically sought information on different institutional requirements for music theory, music history, applied study, AUTOMATICITY EXPECTATIONS 27 performance ensembles, secondary instrument technique classes, laboratory ensembles, conducting courses, field experience, and student teaching. There were also questions concerning specialization of preparation (i.e., band, orchestra) and supplemental educational experiences beyond coursework.
3.) What teaching skills did music teacher education programs expect to be
automatic in their graduating instrumental music teachers, as reported by
music teacher educators?
4.) What performance skills did music teacher education programs expect to be
automatic in their graduating instrumental music teachers, as reported by
music teacher educators?
Examples of the numerous abilities required of music educators were provided in the introductory paragraphs of this chapter. The collection of skills included in the survey instrument were a result of a task analysis conducted by the researcher, which was based on related literature, the InTASC standards, and the NASM competencies.
(see Appendices A and B). This compilation represented (a) psychomotor, cognitive, and affective skills; (b) teaching and performance varieties; (c) Shulman’s (1986,
1987) types of teacher knowledge; (d) diverse levels in Bloom’s revised taxonomy;
(e) the InTASC standards (CCSO, 2010); and (f) the NASM competencies (NASM,
2010). As a result of the undergraduate experiences, coursework, and evaluation addressed in the preceding research questions, what skills did music teacher educators expect to be developed (on an automaticity continuum created for this study) by the time the average, beginning instrumental music teacher entered the profession? AUTOMATICITY EXPECTATIONS 28
An additional exploratory inquiry beyond the four research questions examined potential correlations between reported automaticity expectations and program attributes and evaluation approaches. Was there a connection between what teacher programs offered in terms of coursework and experiences and what music teacher educators expected of their music education graduates? For example, did programs reporting more conducting coursework have statistically significant differences in automatic expectations of conducting abilities? Anonymity of participants was upheld by requesting information in such a manner to make it impossible to identify any specific music education programs or teacher educators.
Data were then generalized from survey responses.
Predictive studies are important in teacher education because of the possibility they provide in foretelling professional success of graduates. If certain experiences or curricula can be correlated with a desirable outcome, the likelihood of success may be increased when these experiences are prescribed. As an example, Brand and Burnsed
(1981) examined the validity of predicting error detection proficiency of undergraduate instrumental music education students based on previous musical abilities. Potential predictors included the number of instruments played, ensemble experience, music theory capabilities, sight-singing, ear training, and number of years of private study. No statistical significance was found between any of the preceding variables and error detection abilities, which might suggest the development of competency in error detection was unrelated to the acquisition of other musical abilities. The authors cautioned music teacher educators from believing error detection abilities could be developed merely through instrumental and theory AUTOMATICITY EXPECTATIONS 29 instruction. If successful acquisition of specific music educator skills is desired, instruction must focus on it. This conclusion might be relevant to this study’s focus on music teacher educator expectations and their potential correlation to undergraduate curricular offerings.
Expectations were examined for suggestive patterns according to diverse categorizations of music educator skills. Each ability from the Automaticity
Expectations section of the survey was included in Appendix A. Also identified in this table were the type of music education skills (i.e., performance or teaching), learning domains (i.e., cognitive, psychomotor, affective) and the different levels of
Bloom’s revised taxonomy (i.e., remembering, understanding, applying, analyzing, evaluating, and creating) (Anderson et al., 2001). The two latter categorizations were not unique to the preparation of educators, but were considered for a variety of learners. The music educator skills that appeared in this survey were also organized according to more teacher education-specific criteria, including Shulman’s (1986,
1987) types of teacher knowledge (content, pedagogical, and pedagogical content), the InTASC standards (based on a professional consensus on the necessary knowledge and abilities of beginning teachers) (CCSO, 2010), and teaching competencies established by NASM (NASM, 2010).
Examples of questions that were explored when analyzing results include:
• Were there greater expectations for psychomotor skills, compared to their
more cognitive counterparts? AUTOMATICITY EXPECTATIONS 30
• Were lower-level abilities (i.e., remembering, understanding, or applying
knowledge) more expected than higher-level tasks (i.e., analysis, evaluation or
creation)?
• Were there differences in expectations between types of teacher knowledge?
• Were there more automaticity expectations for certain InTASC & NASM
criteria?
There is currently no documented connection between the preparation of instrumental music educators and the principle of automaticity. Some mention of automaticity does appear in teacher expertise literature, but mainly is referred to as a novice-expert dichotomy, rather than addressed as a potential development in teacher education. Results from this study may inform the profession about this possibility, as well as further our understanding of how best to prepare beginning music educators who will be successful from the outset of their professional careers. The curricular offerings, evaluation strategies and resulting automaticity expectations reported by music teacher educators may inform the profession about the development of music educator skills in undergraduate music education programs.
Definition of Terms
Affective: An adjective concerning the learning domain of attitudes, emotions, interests, and values, rather than the cognitive and psychomotor domains (Ely &
Rashkin, 2005)
Automaticity: The ability to perform a task without the need for conscious attention (Poldrack et al., 2005); a characteristic of expertise (Ericsson, 1996) AUTOMATICITY EXPECTATIONS 31
Beginning teacher: A certified teacher entering their first teaching position
(Education.com, 2006-2011)
Cognitive: An adjective describing the learning domain of thought processes, as opposed to the psychomotor and affective domains; comprehension represented mentally (Ely & Rashkin, 2005)
Evaluation: Quantitative or qualitative assessment using specified criteria or standards (Anderson et al., 2001)
Expertise: A high level of ability and knowledge developed through education, experience, and practice; distinguished from novice performance
(Ericsson, 1996)
Field experience: Any experience in a school-like context, ranging from non- instructional teaching responsibilities and observation to limited teaching assignments; “hands-on” instructional and non-instructional; student teaching
(Cutietta, 2000 as cited in McDowell, 2007)
Knowledge: Understanding, or being aware of something, as a result of learning and/or experience (Lexicon Publications, Inc., 1989)
Music teacher educator: A collegiate music education faculty member who teaches music education coursework, arranges student teaching placements, collaborates with a cooperating teacher, and/or observes student teachers through the final practicum period of undergraduate teacher preparation (Ely & Rashkin, 2005)
Performance: An activity that is physically represented and observable, similar to the term psychomotor. Musically, it often refers to playing, singing, conducting, or related physical movements (Ely & Rashkin, 2005) AUTOMATICITY EXPECTATIONS 32
Pre-service teacher: An undergraduate student who has declared an education major but has not yet completed the coursework and activities to be a teacher. The pre-service teacher typically completes a period of observing teachers at different levels, followed by an internship or student teaching experience working alongside a mentor teacher before licensed as a professional educator (Education.com, 2006-
2011)
Psychomotor: An adjective referring to the learning domain of physical skills and movement, in contrast to the cognitive and affective learning domains. It involves an observable behavior (Ely & Rashkin, 2005)
Skill: A psychomotor or cognitive task that has become proficient through a process of instruction, learning, and practice, but may also be influenced by other factors such as prior knowledge, experience, age, and ability (Ely & Rashkin, 2005)
Teaching: The process of imparting to another the knowledge or abilities which are possessed by the teacher (Lexicon Publications, Inc., 1989)
Delimitations
This study quantified music teacher educators’ descriptions of instrumental music teacher education programs and their expectations of automaticity as a consequence of that preparation, rather than documenting observable behaviors, which would have required more direct assessment. Therefore, the researcher relied on self-reported data, which may have been subject to participant backgrounds, incomplete perspectives, inaccurate memories and bias, misrepresentation, or participants reporting what they believed the researcher wanted to find (Leedy & AUTOMATICITY EXPECTATIONS 33
Ormrod, 2010). Self-reported data were necessary in order to obtain many diverse perspectives.
The inclusion of only self-described instrumental music teacher educators in the sample was related to the issue of incomplete perspectives, and represented a second delimitation. These individuals might have been responsible for only a small percentage of coursework in the music teacher education curriculum at their institution. Therefore, they might not have had thorough knowledge concerning other courses that contributed to the development of professional knowledge and competencies in pre-service music educators.
The researcher also did not provide definitions of qualifiers, such as “good”,
“basic”, or “professional” that appeared in the survey instrument, relying on the evaluative abilities of music teacher educators. To accommodate for these delimitations, participants had the option of indicating “unknown” for most of the curricular and automaticity expectation questions. Hopefully, this helped insure that the responses received on these questions were from music teacher educators with firsthand knowledge of the requested information.
An additional delimitation of this study was its examination of only the undergraduate preparation of instrumental music educators. Many institutions now offer graduate degrees with teacher licensure for students who received an undergraduate education in some subject other than music education. There are also programs providing alternative certification routes for music educators, in which requirements and experiences might be very different. While these other methods of music teacher education are also responsible for the development of music educator AUTOMATICITY EXPECTATIONS 34 skills in pre-service teachers, the characteristics and background experiences of learners would likely be very different from the undergraduate pre-service teacher who enters the music teacher education program at approximately eighteen years of age and begins teaching in their early to mid-twenties.
The focus of this study was broad with the inclusion of multiple teaching and performance skills representative of different learning domains. Concepts of automaticity might have already been incorporated into music teacher education coincidentally in the development of conducting abilities (Lonis, 1993). There was also an obvious relationship between automaticity and the psychomotor acquisition of performance skills. These types of tasks could easily have been the primary concentration of a study on automaticity in music teacher education. However, in order to present a larger picture of music teacher education, performance and conducting competencies were included among the many diverse abilities required of the beginning instrumental music teacher.
AUTOMATICITY EXPECTATIONS 35
CHAPTER 2
Literature Review
The structure of the following chapter is based on the three stages of
Backward Design through the identification of: (a) desired results, (b) evidence pointing toward the acquisition of these outcomes, and (c) curriculum to assist in the acquisition of these results. The concept behind this form of instructional design with the end in mind (Wiggins & McTighe, 2005). The first stage determines what students should be able to know, understand, and accomplish. The broad topics of expertise and automaticity will be discussed in this section, as well as what research reveals about the teaching skills necessary for beginning and expert teachers. The second stage of Backward Design involves determining what sort of evidence would show the acquisition of the previously mentioned desired results. This section of the literature review will address how skills are acquired, what happens neurologically when abilities become automatic, and how competencies can be assessed. The final stage concerns the actual planning of instructional experiences. This section of the literature review will discuss undergraduate music teacher education curriculum.
Desired Results
Expertise. One desired result, if not of music teacher education, then of that preparation combined with years of experience, is the eventual attainment of professional expertise. Expertise is currently one of the most active areas in cognitive science and psychological research, concentrating on domains such as chess, music performance, teaching, computer programming, and medical diagnosis (Lehmann &
Ericsson, 1997; Patel, Kaufman, & Magder, 1996). By studying experts across many AUTOMATICITY EXPECTATIONS 36 fields, expertise has been attributed to skill acquisition and physical adaptations, rather than innate talent (Ericsson & Charness, 1994). One of the goals in this field of research has been to better understand how the superior abilities of experts are developed in order to improve the training of others (Lehmann & Ericsson, 1997).
Suzuki stated: “Knowledge is not skill. Knowledge plus ten thousand times is skill” (Starr & Starr, 1983, p.13). His belief not only reflects the distinction between knowing about a skill and knowing how to execute it, it also is very similar to the time necessary for the development of expertise. In order to achieve expertise, researchers have found at least ten years, or approximately ten thousand hours, of deliberate practice must be completed (Ericsson, Krampe, & Tesch-Römer, 1993).
The expert is not a genius, but rather an individual who has focused his attention on a certain task for a lengthy amount of time (Welker, 1991).
Deliberate practice is not merely mindless repetition of a task. It has certain defining characteristics:
• It is purposefully engaged in to improve certain components and incorporate
improvements into the resulting performance.
• It is not necessarily enjoyable.
• Practitioners are motivated by the potential to progress.
• True deliberate practice can only be effectively engaged in for approximately
four hours each day.
• While it may eventually develop into an automatic performance, deliberate
practice itself must be consciously attended to in order to be effective. AUTOMATICITY EXPECTATIONS 37
Its lack of intrinsic motivation makes it distinct from play, while it also differs from work because it is not engaged in for extrinsic rewards like social recognition or financial gain. The primary reason individuals put forth such effort is to improve their performance of specific skills (Ericsson et al., 1993). What constitutes practice varies by domain. Some practice is more cognitive, relying on mental activity, as in chess.
Other skills are more physically practiced, such as the psychomotor practice in athletic domains. Still others are a combination of mental and physical practice, for instance in musical performance (Bond et al., 2000).
Some researchers have argued that more may be necessary for the development of expertise than simply putting in the required amount of deliberate practice (Campitelli & Gobert, 2011; Detterman & Ruthsatz, 1999; Meinz &
Hambrick, 2010; Ruthsatz, Detterman, Griscom, & Cirullo, 2008). Ericsson (et al.,
1993; 2004) himself proposed that if the novice hoped to become an expert, he would also need to have access to the right teachers, material, and facilities. Furthermore, others in the domain who were more experienced and knowledgeable were essential in guiding deliberate practice toward expertise. These individuals were sources of feedback, goal setting, and monitoring. Through their research on sight-reading abilities of pianists, Meinz and Hambrick (2010) speculated that working memory capacity also had a role in the development of expertise. If the capacity was too limited, no amount of deliberate practice would result in expertise. However, deliberate practice was still recognized as necessary.
Others theorized that expertise was also a result of general intelligence and domain-specific skills, in addition to deliberate practice. In re-analyzing the findings AUTOMATICITY EXPECTATIONS 38 in Ericsson’s et al. (1993) article, Detterman and Ruthsatz (1999) and Ruthsatz et al.
(2008) found that expert violinists appeared to win significantly more competitions as children, thus possibly indicating their expertise was a result of not only deliberate practice, but also innate talent. It was hypothesized that these experts were different from novices even before achieving the specified amount of deliberate practice time.
Researchers proposed the summation theory, in which musical expertise was the result of a combination of general intelligence, domain-specific skills, and deliberate practice. Therefore, expertise would only be achieved by those who were very intelligent, talented, and motivated to deliberately practice. This theory was tested by studying high school band members, university music majors and performers in a conservatory orchestra, but produced rather inconclusive results. Findings suggested conservatory musicians had higher levels of intelligence and innate musical ability and had practiced for a lengthier amount of time. However, practice only appeared to be an influential factor for musicians who had already been selected for their general intelligence and musical ability, thus possibly indicating expertise relied on more than simply deliberate practice (Ruthsatz et al., 2008).
Despite these other possible influential factors, the level of achieved performance is ultimately related to the quantity and quality of deliberate practice that has been accomplished (Ericsson et al., 1993; Kelly, 2003). Practice is essential in terms of memory because it increases the extent and speed of retrieval. If practice can be consistent, retrieval may be more useful (Logan, 1988). Ericsson and Charness
(1994) also credited deliberate practice activities with the possibility of surpassing the limited capacity of working memory, increasing performance speed, and helping AUTOMATICITY EXPECTATIONS 39 individuals physically adapt to practiced tasks. The evidence cited when emphasizing the importance of deliberate practice, however, has been typically based on apparent correlations between the type and length of practice and the subsequent levels of expertise. Shiffrin (1996) warned that such findings are only suggestive, not causative.
In an attempt to further understand the development of expertise, much research has compared the performances and abilities of experts and novices (Beilock
& Carr, 2001; Beilock, Carr et al., 2002; Bergee, 2005; Borko & Livingston, 1989;
Carter, Cushing, Sabers, Stein, & Berliner, 1988; Ericsson et al., 1993; Goolsby,
1996; Kelly, 2003; Leicester, 1990; Peterson & Comeaux, 1987; Sabers, Cushing, &
Berliner, 1991; Westerman, 1991). Ericsson & Charness (1994) pointed out that it was often easier to identify experts who were socially recognized for their abilities, than attempt to observe specific accomplishments. Expert performance was often not as obviously superior to that of others, thus making the difference between what is believed to be expertise and its actual demonstration more relevant.
Glaser & Chi (1988) organized the findings of earlier expertise research from the 1970s and 1980s into seven traits of experts, which are often cited in more recent studies:
• Expertise is limited to a specific domain.
• Experts are able to recognize relevant patterns within the domain.
• Experts can perform domain skills faster and more accurately.
• Experts exhibit superior short- and long-term memory.
• Problems within the domain are understood at a deeper level. AUTOMATICITY EXPECTATIONS 40
• Experts take longer to analyze problems initially.
• Experts are able to monitor themselves effectively while solving problems.
Two additional characteristics integral to the process of expertise were identified by Bereiter and Scardamalia (1993), reinvestment and progressive problem- solving. When individuals engage in normal learning, the effort they expend on problem-solving is eventually replaced by routines, thereby using less mental capacity. What differentiates expertise is what is done after this capacity becomes available. The authors compared an expert and nonexpert teacher in order to illustrate this point. The nonexpert teacher had achieved a level in which many of her activities required less mental and physical effort. Problem-solving was engaged in to eliminate new problems and reduce effort even more. Likewise, the expert teacher had reached an effortless level in many of her activities, but she chose to reinvest her available mental capacity in pursuit of goals she could not previously achieve, thus increasing her teaching abilities. No matter how effective expert teachers become, there will always remain additional challenges. The second characteristic, progressive problem solving, refers to a continuous restructuring of problems once lower levels are solved to enable advanced stages to be reached. These two processes are directly connected to one another.
Bloom (1986) and his colleagues were interested in how talent was developed in different fields. In the 1980s, they sought to study the twenty-five most talented
Americans under the age of thirty-five. No individual in their study appeared to reach the highest level of achievement in their domain in less than twelve years. Most began learning at an early age, especially in the case of musicians and athletes. When AUTOMATICITY EXPECTATIONS 41 surveying the amount of deliberate practice engaged in by these participants, most spent approximately twenty-five hours per week by adolescence. With age, practice time increased to as many as fifty hours per week. Some participants described how they overlearned specific tasks, thereby making them automatic and more permanent in memory. Bloom concluded that time and overlearning were necessary for the development of automaticity in many psychomotor and cognitive skills necessary for expert performance.
Automaticity. Automaticity has been defined as the ability to perform a task with little conscious attention following a sufficient period of learning and practicing.
Once a skill becomes automatic, attention initially reserved for its performance can be utilized for other tasks (Poldrack et al., 2005). As discussed in the introductory chapter, all automatic skills were originally believed to be unintentional, unconscious, uncontrollable, and efficient according to the dual mode view of automaticity. They were labeled as either automatic or nonautomatic depending on these criteria. The more modern understanding of automaticity, the triple mode view, recognizes that some automatic skills cannot necessarily meet all these defining characteristics consistently (Bargh, 1989, 1992; Moors & Houwer, 2007).
Bargh (1989) distinguished between the three varieties of automaticity in the triple mode view (preconscious, postconscious, and goal-dependent) according to certain unique features. The purpose of his article was to persuade readers from adopting the dual mode dichotomy of automatic versus nonautomatic labeling because automaticity was conditional, relying on certain circumstances such as stimuli, goals, and awareness. Table 2.1 outlines Bargh’s criteria for the different AUTOMATICITY EXPECTATIONS 42 types of automaticity. In all three varieties, automatic processes do not require the individual’s full attention or conscious involvement through completion of the task.
Table 2.1
Types of Automaticity in the Triple Mode View
Preconscious Postconscious Goal-dependent Consciousness Occurs prior to Requires Requires conscious consciousness consciousness awareness
Stimulus Must be triggered Must be Must be by a stimulus, but consciously aware consciously aware conscious of stimulus of stimulus awareness of the stimulus is not necessary
Goal n/a n/a Requires goal
Intention n/a Produces an Intentional or unintended unintentional outcome
Other Uncontrollable n/a n/a characteristics Autonomous Involuntary Nearly effortless
Examples First impressions Subliminal Implicit learning Stereotyping preparation of a Habits behavior Driving Typing “Tip of the tongue” phenomenon Note. From Bargh, J.A. (1989). Conditional automaticity: Varieties of automatic influence on social perception and cognition. In J. Uleman & J. Bargh (Eds.), Unintended thought (pp. 1-51). New York: Guilford.
Bloom, perhaps best known for his taxonomy of educational objectives, also conducted research related to automaticity in the 1980s. In a 1984 lecture, he made the following statements concerning automaticity: AUTOMATICITY EXPECTATIONS 43
• It can be understood in each of the taxonomy domains (affective, cognitive,
and psychomotor).
• Automaticity itself is not taught. The skill that is being automatized is the
focus of instruction.
• What becomes automatic is not forgotten (Bloom, 1984 as cited in Lonis,
1993).
Bloom’s thoughts lead directly into the next section’s focus on the characteristics of automatic skills.
Characteristics and benefits of automatic skills. Many of the defining characteristics of automatic skills are also often cited as reasons to develop automaticity for certain tasks. Shiffrin and Schneider (1977), two influential researchers who are cited in most automaticity research, distinguished between controlled and automatic processes. Controlled processes: (a) demand attention,
(b) are limited by the capacity of the working memory, (c) can be learned and modified quickly without much training, and (d) can be used to control the flow of information between the working and long-term memory. Automatic processes, on the other hand: (a) do not depend on or reduce the capacity of the working memory,
(b) do not require attention, (c) may be initiated by the individual but can be completed automatically, (d) will gradually improve as the sequence continues to be learned, and (e) are unaffected by cognitive load. Some parallels can be seen in
Bloom’s (1984 as cited in Bloom, 1986) list of automatic skill features: (a) effortless,
(b) efficient, (c) fast, (d) accurate, (e) occurring simultaneous with other brain functions, and (f) contributing to higher cognitive abilities. AUTOMATICITY EXPECTATIONS 44
Any task complex enough to warrant the attention of automaticity researchers likely has both automatic and controlled features (i.e., driving, stereotyping, attribution) (Shiffrin & Schneider, 1977). Csikszentmihalyi and Nakamura (2010) also believed it was rare for effortless attention to be completely automatic with no controlled components. Holt and Rainey (2002) found most complex tasks performed by experts often utilized both automatic and controlled processes. Training could potentially be more effective if one was able to identify what components were controlled and what were automatic.
The automatization of controlled processes has some benefits, as can be seen in the preceding descriptions of automatic skills. The limited capacity of short-term memory can be used more efficiently and made more available for other tasks, allowing attention to be directed elsewhere (Feldon, 2007a; Poldrack et al., 2005;
Kelly, 2003; Shiffrin & Schneider, 1977). Findings from Holt & Rainey’s (2002) literature review indicated that the benefits of automaticity might be consistent across perceptual, psychomotor, and cognitive tasks. They listed the benefits as: (a) fast, accurate performances with little attention necessary; (b) the ability to direct attention to other types of processing, especially higher-order processes that might not be able to be automated; (c) the capacity to multi-task; (d) improved retention of learned material; and (e) immunity to external distracters such as stress, alcohol, and fatigue.
Ashby et al. (2010) also reported that practically all cognitive and psychomotor abilities improve in speed and accuracy as they are practiced more, with performance improving to such an extent that skills become automatic. Automatic processes AUTOMATICITY EXPECTATIONS 45 appeared to be less affected by interference, and caused less interference with other processes (Holt & Rainey, 2002; Poldrack et al., 2005)
Limitations of automatic skills. Automaticity is not without its disadvantages however. Some of the defining characteristics of automatic skills put forth by Shiffrin and Schneider (1977) could be viewed as more negative than positive, including the convictions that automatic processes: (a) require extensive practice; (b) are difficult to change, ignore, or suppress after becoming automatic; (c) are executed at such a fast pace that individual components often cannot be perceived consciously; and (d) do not produce new learning in the long-term memory.
Tasks that have become automatic may also degrade when an individual must attend to the performance. When one is forced to think about the individual components of an automatic process, execution is slowed. Nonautomatic skills were actually helped by more focused attention, while those which had become automatic were impeded (Beilock, Carr et al., 2002; Bloom, 1986; Syed, 2010). Wan and Huon
(2005) provided a musical example: During the initial learning of a performance skill or piece, processes were much slower and attention could be directed toward each detail. However, after the musician extensively practiced, he could no longer attend to every individual note and its performance technique and still be able to perform at the necessary tempo. Some things must be automatic in order to allow attention to be directed in other musical directions, but this may in turn reduce the ability to attend to intricacies.
Once a skill has become automatic, it is often difficult to control or change. In a way, controlled processes are easier to use because they can be set up, modified, AUTOMATICITY EXPECTATIONS 46 and used in new situations (Mishra, 2005; Holt & Rainey, 2002; Shiffrin &
Schneider, 1977). However, because attention is required for control processes and attention is so limited, only a single process can be effectively performed at one time
(Shiffrin & Schneider, 1977; Holt & Rainey, 2002). If one wants to change or improve an automatic skill, attention must be focused and the skill must be returned to the working memory from the long-term memory (Beilock, 2010).
Choking. A related idea is the belief that once tasks become automatic, they are moved to the implicit, unconscious system of memory (Syed, 2010). The term choking is used to indicate a neural glitch that occurs when individuals explicitly monitor skills that would be more successfully performed automatically. This is sometimes seen in high-pressure situations when a person is unable to perform a skill they had previously mastered. Choking can be viewed as a psychological reversion problem. One switches from using a cognitive system of experts to one used by novices (Baumeister, 1984; Beilock & Carr, 2001; Beilock, Carr et al., 2002; Masters,
1992; Syed, 2010). Choking is also referred to as “paralysis by analysis” (p.5). Not only can this be caused by thinking too much about automatic skills, it can also occur as a result of not paying enough attention to a skill and simply relying on inaccurate routines. When performing under stressful conditions, people try to consciously control their performance, which is often counterproductive. The issue of choking is relegated to expertise. Novices must still attend to skills while they are learning and practicing. Their performance is not hurt by attention while automaticity is still being developed (Beilock, 2010). AUTOMATICITY EXPECTATIONS 47
Choking is similar to the explicit monitoring theory, which blamed performance degradation on the performer’s attempt to control processes step-by-step
(Baumeister, 1984; Masters, 1992). Wan and Huon (2005) explored performance degradation in musical performance, which could occur as a consequence of a slip in memorization or execution of a wrong note. Results of this study supported the previous claims of the explicit monitoring theory: When participants attended to a task that had previously been implicit, the researchers observed more errors. The authors concluded that consciously controlling a well-learned performance may disrupt the automaticity that had been achieved. Degradation was caused by monitoring skills explicitly.
Csikszentmihalyi’s (1990) description of psychic entropy also bears some resemblance to choking. This condition was defined as the interference of consciousness by information that threatens one’s goals, which might effect one’s attention and ability to pursue the desired goal. The tendency to choke on otherwise expertly performed psychomotor skills occurs frequently in situations where performers are working hard to succeed. Masters (1992) proposed that choking was caused by attending to explicit knowledge of a skill. Performers could possibly avoid choking if they had little or no knowledge of the skill’s rules. Indeed, results of his study supported his hypothesis: Participants with less explicit knowledge were unlikely to choke under pressure because reinvestment of consciousness did not affect automaticity to such an extent.
AUTOMATICITY EXPECTATIONS 48
Expert-induced amnesia. Another limitation concerns the difficulty in analyzing or explaining the individual components of an automatic task (Holt &
Rainey, 2002). Psychomotor skills can become so ingrained that one cannot communicate the individual steps of the process. The ability has become completely implicit. This is known as expert-induced amnesia (Beilock & Carr, 2001; Beilock,
Wieranga et al., 2002; Syed, 2010). Automaticity and expertise can sometimes be detrimental to teaching because experts often forget what is easy or difficult for novices (Bransford, Brown, & Cocking, 1999; Sternberg, 1996). Experts may no longer think in terms of the individual steps they are carrying out (Beilock, Wieranga et al., 2002).
Experts may have evolved so far from the novice level that they can no longer predict the problems or mistakes novices may encounter in their domain. Beilock
(2010) referred to this as the “curse of expertise” (p.14). When one becomes proficient at a skill, they typically forget what it was like to function in their initial stages of learning. The numerous steps that had to be thought through have merged into a single, procedural memory. The expert often has trouble explaining all the individual steps that they are performing because they are no longer cognizant of those separate actions. A procedural memory is implicit and unconscious. When one becomes good at a skill, it is performed too quickly to allow for conscious attention.
As a skill improves, the performer’s explicit memory decreases. For this reason,
Beilock concluded that it is often harder for experts to teach their well-learned abilities.
AUTOMATICITY EXPECTATIONS 49
Adaptive expertise. Automatization of certain skills has been the focus of much research in the field of expertise development. However, Ericsson (1996) argued that this focus has been too narrow. While automaticity is important, it is simply one characteristic of expertise. Ericsson (1998, 2004) further contended that the acquisition of expertise might be detrimental to performance because experts would not be as successful in adapting to different conditions. Adaptations would require nonautomatic control if the expert’s performance required modification. “The key challenge for aspiring expert performers [would be] to avoid the arrested development associated with automaticity” (Ericsson, 1998, p.90). Contrary to this,
Shiffrin and Schneider (1977) believed automaticity could be advantageous to adapting to different circumstances. Automatic processes might enable individuals to adjust to changes in the environment that might make previously learned processes ineffective or dangerous. New situations could also be dealt with even when prior knowledge was not applicable. Finally, processes could become more complex as the individual built upon prior learning and automatic skills.
Feldon (2007b) also asserted in his literature review that other studies have challenged Ericsson’s thoughts on automaticity by making a distinction between routine and adaptive expertise in addition to the transferability of automated skills to new situations. For example, Hatano and Inagaki (1986) compared the routine and adaptive varieties of expertise. Routine experts had highly developed speed, accuracy, and automaticity in certain abilities, but often were not as flexible or adaptable when confronted with novel situations. Procedural knowledge continued to be effective when solving typical problems. Adaptive expertise, however, was more essential AUTOMATICITY EXPECTATIONS 50 when adjustments had to be made to unforeseen problems or contexts. Holt and
Rainey (2002) used the term adaptive thinking in the military context to illustrate the ability to use knowledge and reasoning to adjust to unanticipated events in a expert’s domain. Bereiter and Scardamalia (1993) comparison of crystallized and fluid expertise also supports the concept of adaptive expertise. Crystallized expertise referred more to established routines that were used for specific tasks. In contrast, fluid expertise involved well-learned skills that were employed for new or challenging tasks.
Feldon (2007b) suggested two factors were essential when considering automaticity and adaptive expertise. To start with, more automated components within a larger complex skill must be automatized in order to increase the working memory’s capacity to consciously adapt. Additionally, certain decision points must exist to allow experts to adapt to the novelty of the task. He cited Lehmann and
McArthur’s (2002) description of expert sight-reading in music. Specific abilities, such as deciphering notation, recognizing patterns, and executing performance skills, must be relatively automatic in order to allow the sight-reader to attend to the new music. The musician cannot still be consciously learning how to read music or perform their instrument in order to effectively sight-read.
Specific teaching routines may be able to be automatized, but teachers’ actions are still influenced by the needs of their students and unanticipated classroom events. Many facets of teaching occur in response to the moment (Hammerness et al.
2005). Such arguments by teacher researchers advocate for more adaptive expertise, but do not completely abandon automaticity in teaching. Berliner (2004) described AUTOMATICITY EXPECTATIONS 51 adaptive expertise among expert pedagogues in the attributes of lifelong learning, the use of their expertise to solve new problems, and the connection of new information to prior knowledge. Teachers who did not continue learning while teaching were probably no longer teachers (Haack, 2003). Teacher education should help develop novice teachers into adaptive experts and lifelong learners (Hammerness et al., 2005).
One of the potential limitations mentioned by some researchers (Mishra,
2005; Holt & Rainey, 2002; Shiffrin & Schneider, 1977) was the loss of flexibility in automatic performances. However, humans, even experts, are not automatons. Skills are rarely so inflexible to be machine-like. Flexible adaptations to automatic tasks are always being made. Some procedures may have become automatic, but this does not mean the performance is monotonous. With more mental resources available, individuals have the capacity to come up with creative ideas that can contribute to the automatic performance (Bereiter & Scardamalia, 1993). Gentner (1988) examined the acquisition of typing, as an example of a psychomotor skill, in respect to expertise.
He assumed at the outset of this research that expert typists would perform inflexibly as a result of their extensive practice. However, individuals appeared to be very adaptable in the use of their skill, displaying sensitivity to potential opportunities and limitations of the task and adjusting accordingly.
“Rigidity, indeed, is the mark of the failed expert” (p. 109). Inflexibility can be evaded by confronting new problems. Automaticity will not inevitably produce rigidity if previously acquired abilities are viewed as steps toward developing other automatic skills (Bereiter & Scardamalia, 1993). Hatano and Inagaki (1986) also proposed three contributing factors toward the development of adaptive expertise: AUTOMATICITY EXPECTATIONS 52
(a) repetitive practice paired with random variations, which would require adjustments; (b) encouragement of experimentation in how skills are performed; and
(c) modification of values from performing quickly and accurately to developing deeper understandings.
Expertise in teaching. “Putting into words the many combinations of knowledge, technical skills, and instructional expertise of an accomplished music teacher is like trying to describe the intricacies of a complex musical composition”
(National Board for Professional Teaching Standards (NBPTS), 2001, p.2). In this section of the literature review, the term “expert” is used synonymously with
“accomplished” and “effective” for clarity purposes. No sources included in this review differentiated between these three descriptors. All signified high quality teaching. In order to organize the potential characteristics of expert teachers, sources were analyzed for emerging patterns and larger categories. Initially, this resulted in four groups: (a) experience, (b) personal traits, (c) knowledge of students, and
(d) schemata. Remaining characteristics, which did not fit well into the preceding categories and did not appear with the same frequency, were sorted according to
Shulman’s (1986, 1987) framework for teacher knowledge: (a) content knowledge,
(b) pedagogical knowledge, and (c) pedagogical content knowledge. Finally, four additional categories emerged, related to the focus of this study: (a) automaticity,
(b) problem-solving skills, (c) flexibility, and (d) recognition of patterns.
“The characteristics of effective teachers have been extensively discussed, making the compilation of a comprehensive list of traits very difficult” (Steele, 2010, p.71). Sternberg and Horvath (1995) believed teaching expertise should be viewed, AUTOMATICITY EXPECTATIONS 53 not as a set of required characteristics, but as a collection of similarities shared by many expert teachers. Furthermore, what is considered effective teaching may differ between cultures (Ballantyne, 2007b). “Currently, there is no working model of expertise for teachers of the arts” (Sogin & Wang, 2002, p.12). However, a model of teaching expertise is necessary in order to identify the ultimate goal of preparation, to recognize teachers for the achievement of expertise rather than the simple attainment of seniority; and to distinguish experts from experienced novices (Sogin & Wang,
2002).
The question of what it means to be an expert teacher has taken on some
urgency in the nationwide effort to reform public education. If American
public schools are to become centers of excellence, then their most important
human resource (i.e., teachers) must be effectively developed. To know what
we are developing teachers toward, we need a model of teacher expertise . . . a
model with which to inform our performance standards – to distinguish those
teachers who are expert at teaching students from those who are merely
experienced at teaching students. (Sternberg & Horvath, 1995, p.9)
Attempts to study pedagogical expertise has been hindered in the past because of the difficulty in identifying expert teachers, the debate concerning the relevance of talent versus deliberate practice, and the dilemma of correlating expert teacher performance with student performance (Berliner, 2001, 2004). Berliner (1986) provided several reasons why research in teacher expertise was important:
• Knowledge about expert teachers’ routines, scripts, and schema could assist in
the future identification of less expert teachers. AUTOMATICITY EXPECTATIONS 54
• The definition of expert teaching could provide a starting point on which the
learning of novices could be scaffolded.
• Examples of expert performances could be a learning tool.
• The concept of pedagogical expertise could be promoted.
• Expertise in the actions of practicing teachers might be more easily
articulated.
Bond, Smith, Baker and Hattie (2000) also claimed teacher expertise could not be identified by a collection of simple characteristics because of its complexity and the difficulty of assessing expertise in teaching. These researchers studied the
NBPTS perspective of accomplished teaching, which is a system of advanced certification designed to recognize excellence in teaching. The standards established by this organization are intended to differentiate between accomplished and competent teachers, rather than simply good and bad teachers. Results of the study showed nationally certified teachers obtained higher mean scores on dimensions of teaching excellence when compared to non-nationally certified teachers. Sampled certified teachers exhibited more characteristics similar to those defined in teacher expertise research. These results are further described in the following subsections.
Research does not provide a clear definition of music teacher expertise
(Browning, 2007; Duke & Simmons, 2006). Identification is often limited to teaching experience, competitive performance ratings, and acknowledgement by colleagues.
Eight characteristics, particular to expert music educators, were proposed in
Browning’s (2007) literature review: AUTOMATICITY EXPECTATIONS 55
• Behavioral expectations are disclosed to students through the efficient use of
rules, procedures, routines, and signals.
• Information and questioning is structured and communicated well.
• Guided practice is organized effectively.
• Content knowledge is displayed correctly and musically.
• Assessment and feedback of student comprehension is accurate.
• Musical activities are chosen appropriately. Furthermore, the expert teacher
has a well-developed aural picture in mind.
• The focus of the teacher’s attention is on developing a musical performance
and rectifying important mistakes.
• The teacher maintains a fast pace of instruction.
Research also frequently showed differences between novice and expert music educators in utilization of instructional time, instructional focus, decision-making, and ability to direct attention while conducting.
Kelly (2010) asked cooperating teachers what skills and behaviors they thought were most important in developing effective music teachers. Those rated highest were generally more social and involved personal characteristics, while others that did not directly use musical skills, knowledge, or instructional techniques were considered least important. The researcher concluded that music teacher education programs should incorporate an element that helps pre-service teachers develop personal characteristics and values necessary for effective teaching.
There are also contrasting, less optimistic views of expert teachers. Bereiter and Scardamalia (1993) asserted experts were easiest to recognize when they were AUTOMATICITY EXPECTATIONS 56 conspicuously different from normal people. This book was about the development of expertise in general, rather than particular to teaching expertise. However, the authors subsequently stated one of the reasons the identification of expert teachers was problematic was that everyone was able to and often did teach to some degree.
An additional point of view appears in the 2001 No Child Left Behind act.
Highly qualified teachers were described as having a college degree, possessing full teacher certification or licensure, and demonstrating content knowledge through a written test. If teacher effectiveness was solely based on these criteria, however, some novice teachers could meet the requirements, without the experience and effectiveness of expert teachers (Palmer, Stough, Burdenski, & Gonzalez, 2005). The following paragraphs attempt to identify more robust characteristics that have emerged in recent literature, with the intent to define the desired skills music teacher education program aspire to instill in their pre-service teachers.
Experience. Expertise in teaching is often paired with experience (Browning,
2007; NBPTS, 2001; Palmer et al., 2005). This connection makes sense in regard to expertise research findings on the need for ten years or ten thousand hours deliberate practice (Ericsson et al., 1993). However, just as some sources earlier in the literature review claimed more was necessary than a lengthy amount of practice time
(Campitelli & Gobert, 2011; Detterman & Ruthsatz, 1999; Ericsson et al., 1993;
Ericsson 2004; Meinz & Hambrick, 2010; Ruthsatz et al., 2008), researchers of teacher expertise have found experience in teaching does not equate with expertise in teaching (Hogan, Rabinowitz, & Craven, 2003). While it is impossible to become an expert without extensive experience, it alone does not lead individuals to develop AUTOMATICITY EXPECTATIONS 57 expertise (Browning, 2007; Ericsson, 1998, 2004). Sogin and Wang (2002) found expert music teachers in their study were distinguished from novices in the amount of education received. The average years of experience, however, did not predict expertise: Those deemed experts had an average of 7.8 years experience, while novices had been teaching for an average of 8.36 years.
Expertise in teaching was defined by years of experience in some studies reviewed by Bond et al. (2000), but others argued that the complexity of teaching distinguished it from some domains of expertise studied in cognitive psychology.
Browning (2007) argued that, even though it is often used, experience alone was not a reliable method of identifying teacher expertise because many had extensive experience but had not attained the performance or knowledge level of experts.
According to Ericsson and Charness’s findings (1994), most individuals do not continue to engage in extensive deliberate practice once they are able to perform the tasks necessary for employment at a satisfactory level. Only a weak correlation was indicated between the amount of experience and the expected performance level.
The level of acquired performance skills appeared to be lower for those who only had experience, compared to those who also engaged in specific deliberate practice activities (Ericsson & Lehmann, 1996).
Individuals who learn to teach may progress through five different stages as they acquire their professional skill: Novice, Advanced Beginner, Competent,
Proficient, and Expert. Educators who are student teaching up through their second year in the classroom often exhibit the characteristics and needs of the Novice and
Advanced Beginner levels. As experience is gained, higher-level stages are usually AUTOMATICITY EXPECTATIONS 58 reached. However, these more advanced levels cannot be expected until teachers have had the experience (or deliberate practice) of full-time teaching for a few years.
Young teachers regularly have to spend much of their time learning and focusing on the actual activity of teaching. According to this model, it is possible for all teachers to attain competence, while only some will reach the proficient stage and even fewer, the expert stage (Berliner, 1988). When contemplating the potential length of time necessary to achieve expertise in teaching, literature reviewed by Berliner (2004) estimated five to seven years depending on the level of effort put forth. “Competent” teaching was likely achievable within two years with effort put forth.
Personal traits. “ . . . Teaching is a job that can be learned; one need not be
‘born’ a teacher in order to teach effectively” (Reynolds, 1995, p. 200). NCATE lists professional dispositions among its standards in the preparation of teachers. However, what these dispositions are exactly is not specified, except the belief that all children can learn (NCATE, 2008). Other sources point to certain personal qualities often displayed by expert teachers. Prior to their teaching career, they often performed well academically (Polk, 2006). Once in the classroom, these teachers showed greater respect for the learner, possessed a caring nature, and valued diversity (Bond et al.,
2000; Jessop, 2004; NBPTS, 2001). Expert teachers generally held high standards for themselves, and were able to reflect on their professional effectiveness (NBPTS,
2001). Such teachers were also professional in their ability to work with others, continued to develop as a teacher, made contributions to the profession, were recognized socially for their professional role, and held professional memberships AUTOMATICITY EXPECTATIONS 59
(NBPTS, 2001; Palmer et al., 2005; Polk, 2006). Above all, they exhibited an clear passion for teaching (Bond et al., 2000).
Knowledge of students. NBPTS (2001) provided a generic statement that expert teachers required knowledge of students. Others sources were more specific about the types of understanding this could include. Elliott (1992) included psychology, child development, and learning physiology among necessary formal knowledge. While teaching, expert teachers were better able to work with students
(NBPTS, 2001) and monitor their learning (Bond et al., 2000). They were also able to engage student interest through eye contact, body language, facial expressions, speed and volume of speech, modeling, feedback, and positive reinforcement (Mark &
Madura, 2010). Expert teachers were able to understand the method and the reasons behind student success and failure in academics. With their substantial previous knowledge, learning tasks suitable to the needs of students could be provided (Bond et al., 2000).
Schemata. Ormrod (2008) defined schemata as closely connected ideas that influence how new information is perceived, processed, and remembered. Prior experiences and knowledge are organized into schemata to make sense of future experiences and learning. In a speech by Shavelson (1986, as cited in Borko &
Livingston, 1989), schemata of teacher knowledge were categorized into scripts
(familiar, every day experiences), scenes (people or objects), and propositional structures (the teaching-learning situation). Expert teachers developed larger, more organized schemata on which to draw on. To be successful, teachers must have a large collection of strategies, routines, and information that can be referred to while AUTOMATICITY EXPECTATIONS 60 teaching (Borko & Livingston, 1989; Polk, 2006) and performed effortlessly
(Leinhardt & Greeno, 1986).
The hypothesis that experienced teachers have more developed schemata was studied by comparing novice and experienced high school teachers’ abilities to recall and analyze problem while teaching. Experienced teachers exhibited more recollection of classroom events and analyzed these events using more procedural knowledge. This difference suggested experienced teachers might have developed more elaborate schemata related to teaching. Furthermore, the intensity of analyses while problem-solving might have been indicative of their schemata and their underlying procedural knowledge (Peterson & Comeaux, 1987). Sabers et al. (1991) discovered differences in the schemata of expert and novice teachers, which enabled them to be more attentive to multiple, concurrent events related to both teaching and learning in experimental classroom contexts. Butler (2001) reported that schemata of expert teachers appeared more multifaceted, connected, and easily retrieved. They were able to plan more efficiently, combining information from their schemata into lesson activities. They were also better at predicting where students might have difficulty with the content (Borko & Livingston, 1989). Pedagogical and subject- matter knowledge, as well as prior personal experiences and beliefs, are relied upon when expert teachers make decisions. Therefore, success is dependent on the knowledge and experience available to teachers. Pre-service teachers can become more proficient at making decisions by developing more intricate schemata (Butler,
2001). Carter et al. (1988) also found that expert teachers had more developed schemata, enabling them to: (a) process and sort visual information by relevance, AUTOMATICITY EXPECTATIONS 61
(b) make connections between different elements observed in the classroom, and
(c) comprehend educational situations contextually. This suggested that experienced teachers used their prior knowledge and experience to understand the many intricacies of the classroom environment.
Teacher knowledge. Additional expert teaching characteristics were divided between content, pedagogical, and pedagogical content knowledge, the three main types of teacher knowledge defined by Shulman (1986, 1987). He identified a total of seven varieties:
• content;
• pedagogical;
• curriculum;
• pedagogical content;
• learners;
• educational contexts; and
• goals, purposes, values, philosophy, and history of education.
Millican (2008) modified Shulman’s (1986, 1987) types of teacher knowledge into the following illustration, which is useful in visualizing the relationship between categories (See Figure 2.1). High school instrumental teachers were surveyed to ascertain their perceptions of knowledge required to be an effective teacher, using
Shulman’s categories. Participants ranked pedagogical content knowledge as most important, followed by content and general pedagogical knowledge. These top three rankings were much higher than the other forms of knowledge (curriculum, learners, educational contexts, and administrative knowledge). However, the researcher noted AUTOMATICITY EXPECTATIONS 62 that the lower ranked items were not considered unimportant, just less important in comparison to other types (Millican, 2008).
Figure 2.1
A Depiction of Shulman’s Types of Teacher Knowledge
Note. From Millican, J.S. (2008). A new framework for music education knowledge and skill. Journal of Music Teacher Education, 18(1), 67-78; Shulman, L.S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 4-14; and Shulman, L.S. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational Review, 1-21.
Content knowledge. Content knowledge is defined as the ability to understand concepts in a particular domain (Shulman, 1986, 1987). It is synonymous with subject matter knowledge. Some articles were vague in the level of understanding expert AUTOMATICITY EXPECTATIONS 63 music teachers must attain, indicating merely the need for musicality (Jessop, 2004) or general subject knowledge (Polk, 2006). NBPTS (2001) presented more detailed descriptions of the content knowledge characteristics necessary for music educators.
Some continued to put forth broader categories, such as musical knowledge and historical/cultural knowledge. However, the board went on to state content knowledge should be demonstrated through performance, conducting, creative musical activities, and knowledge of theory, history, and repertoire. Mark and Madura (2010) further clarified that the expert music teacher should be a capable musician, performer, conductor, and accompanist, as well as possess abilities in sight-reading, error detection, music history, and arranging.
Two articles deserve mention in this section because of their unique views of expertise specific to music education. Elliott (1992) defined two necessary forms of music educator expertise: musicianship and educatorship. Both represented action- based, procedural knowledge. Musicianship was essentially content knowledge.
Elliott made the assertion that the capacity to explain or identify a specific performance technique did not mean the individual had a working understanding of that technique. To truly comprehend it, the learner had to be able to demonstrate the technique, which was more of a behaviorist approach to learning. Educatorship referred to actual teaching expertise. Not only was musical knowledge required, teachers also had to be able to convey that knowledge to learners. “Teaching expertise is the flexible situated ability to think-in-action in relation to student needs, subject matter standards, community needs, and the professional standards that apply to each and all of these” (p.13). AUTOMATICITY EXPECTATIONS 64
Duke and Simmons’ (2006) search for music teacher expertise was very different than other research because it was a qualitative study of three renowned teacher-artists. The authors organized nineteen characteristics shared by each of these teachers into three categories: Goals and expectations, effecting change, and conveying information. Some of these characteristics could have applicability in the classroom music context (see Table 2.2).
AUTOMATICITY EXPECTATIONS 65
Table 2.2
Characteristics of Expert Music Teachers
Category Characteristics Goals and Appropriate repertoire was chosen to correspond with the student’s ability. Expectations Teachers had a clear sound of the music in mind to which they were working the student.
A good tone was always demanded.
Lessons focused on specific technical and musical goals.
Teachers guided the student to a target that was close enough to their current level to be achievable during the lesson.
Teachers recalled what had been worked on previously with the student to point out positive and negative development.
Effecting Pieces were run through and assessed like performances. Change Mistakes halted the lesson immediately.
Passages that were being corrected were repeated until performed accurately.
Lessons were fast paced.
Teachers seemed to have an intuition about when to slow the pace and lessen the intensity.
Students were allowed to make choices from a limited number of teacher-supplied options.
Conveying Teachers were very detail-oriented and communicated this to the students. Information Technique was described more in the outcome of sound, rather than the physical motion.
Technical feedback was supplied by creating an interpretive effect.
Teachers utilized high quality modeling.
Note. From Duke, R.A. & Simmons, A.L. (2006). The nature of expertise: Narrative descriptions of 19 common elements observed in the lessons of three renowned artist-teachers. Bulletin of the Council for Research in Music Education, 170, 7-21.
The authors suggested these characteristics appeared in the teaching of each of the participants because they represented the highest form of music education and the way students learned best. AUTOMATICITY EXPECTATIONS 66
Expert teachers utilize and organize their content knowledge differently, typically possessing a deep understanding of the content to be taught. These teachers are also able to integrate new knowledge into students’ prior understanding, which allows them to correct student misconceptions, advance student comprehension, and direct new learning (Bond et al., 2000). Nevertheless, content knowledge alone is useless in the development of teacher expertise when unaccompanied by pedagogical knowledge (Shulman, 1986).
Pedagogical knowledge. Pedagogical knowledge refers to the comprehension of classroom teaching skills (Shulman, 1986, 1987), such as how to organize instruction and manage the classroom. Similar to other categories, some sources merely mentioned pedagogical knowledge was necessary for teaching expertise without further clarification (Polk, 2006; Jessop, 2004). This type of knowledge refers more to general teaching skills, regardless of the subject (Polk, 2009; Shulman,
1987). Characteristics mentioned in the literature that could be attributed to pedagogical knowledge included the abilities to:
• communicate (Millican, 2009; Polk, 2006);
• model concepts (Polk, 2006);
• make decisions (Bond et al., 2000);
• engage in planning at different levels (annually, daily, and concurrently with
teaching);
• keep the lesson on track while responding to student needs (Borko &
Livingston, 1989);
• plan and implement assessment (NBPTS , 2001); AUTOMATICITY EXPECTATIONS 67
• manage the classroom; and
• establish classroom rules and routines (Millican, 2009).
Pre-service and experienced music teachers believed pedagogical abilities were more important than musical skills (Rohwer & Henry, 2004).
Expert teachers exhibit a higher level of pedagogical knowledge in their distinct abilities to observe and comprehend classroom events, their analyses of teaching strategies and techniques, and their ability to attend to many simultaneous events occurring at any time in the classroom (Sabers et al., 1991). Experienced band teachers, whose use of rehearsal time was compared to novice and student teachers, exhibited the following characteristics: (a) increased break time during class, (b) more equal division of rehearsal time between warm up and musical selections, (c) use of more than half of the rehearsal for performance, (d) a propensity for nonverbal modeling, (e) the ability to get the students on task quickly, and (f) the least verbalization during rehearsal. Student teachers, in contrast, talked most and had the students perform least during rehearsal (Goolsby, 1996). Beyond all the previous descriptors, three varieties of pedagogical knowledge and skill were identified as significant predictors of teaching success in any subject: classroom management, presentation, and organization (Millican, 2009).
Pedagogical content knowledge. Shulman (1987) described pedagogical content knowledge as “the blending of content and pedagogy into an understanding of how particular topics, problems, or issues are organized, represented, and adapted to the diverse interests and abilities of learners for instruction” (p.8). It is different from AUTOMATICITY EXPECTATIONS 68 general pedagogical knowledge because it is specific to the subject area (Grossman,
Schoenfeld, & Lee, 2005). It includes comprehension of:
• topics regularly taught in the subject;
• methods and strategies that can help learners understand the topic
(e.g., representations, analogies, illustrations, examples, explanations, and
demonstrations);
• effective strategies proven by research and experience;
• material students often struggle with; and
• prior knowledge or misconceptions students usually bring to the learning
situation (Shulman, 1986).
Pedagogical content knowledge brings together understanding of the subject being taught, as well as understanding of how best to teach it to the student population at hand. Both Elliott (1992) and Duke and Simmons (2006) emphasized the need to convey content knowledge to the learner. Expert teachers tended to use their knowledge more proficiently and have an extensive amount of pedagogical content knowledge (Bond et al., 2000). By knowing their subject and learners well, priorities for instruction could be developed (Jessop, 2004). NBPTS (2001) stressed teachers should be facilitators of musical learning through: (a) curriculum development; (b) teaching proficiency; (c) knowledge of instructional materials, strategies, and highly specialized skills; and (d) competency in at least one musical specialty. Ballantyne and Packer (2004) provided music-specific examples of pedagogical content knowledge, which included “knowledge of music teaching techniques, engaging students with music in a meaningful way, implementing the AUTOMATICITY EXPECTATIONS 69 music curriculum effectively, assessing students’ abilities in the various aspects of music, and explaining and demonstrating musical concepts” (p.302).
Beginning teachers in Ballantyne and Packer’s (2004) study ranked pedagogical content knowledge as the most important type of teacher knowledge but also the least addressed in teacher education. Overall, participants desired more preparation in pedagogical content knowledge and skills during their methods coursework. Pedagogical and pedagogical content knowledge typically do not begin to develop until novice teachers are faced with the realities of actual teaching
(Feiman-Nemser & Buchanan, 1986; Shulman, 1987).
Automaticity. Some of the earliest pedagogical expertise research found that much of the teacher’s “knowing-in-action” occurred as a result of automatization of some teaching skills (Berliner, 1986, p.7). Expert teachers displayed the ability to automate routines or repetitive tasks in the profession (Berliner, 2001, 2004).
Hammerness et al. (2005) defined teacher expertise as the ability to perform a variety of activities without having to think about how to perform them, an idea very reminiscent of the principle of automaticity. Specific activities were mentioned, such as: how to manage a class that is doing group work, how to give directions, and how to hand out material while retaining student attention. Berliner (1988) also specified that the “fluidity and effortlessness” (p.14) seen in the performance of expert teachers might be the result of established routines for specific tasks, such as taking attendance, dealing with homework, and leading discussions.
Leicester (1990) hypothesized that automaticity would be a characteristic of expert teachers in his study of rapid, clinical judgments. These were defined as AUTOMATICITY EXPECTATIONS 70 teachers’ use of knowledge in making decisions while simultaneously instructing students. This concept was comparable to automaticity because decisions had to be made while performing other activities and with little time to consciously focus on the decision. Automaticity was assumed to develop with experience and allow the teacher to direct more attention to needs in the classroom. The study’s findings supported his hypothesis. The teachers themselves recognized that tasks, which had been extensively practiced, assisted with classroom management as less conscious effort was necessary and instruction could be returned to more quickly. Automaticity was also evident in the difficulty expert teachers displayed in communicating information. For example, some would describe how they intuitively knew when and how to respond in the classroom and were unable to provide explicit details. Expert teachers may actually not be aware of many of the automatic procedures they frequently use (Berliner, 1986).
Experts, in general, are more capable of directing their attention to other demands while performing an automatic task (Beilock, Carr et al., 2002). Novice teachers, on the other hand, often are concerned with remembering the details of the lesson plan they have prepared, thereby distracting some of their attention away from the classroom and interactions with their students. In contrast, expert teachers are able to retrieve knowledge from their long-term memory while focusing on how the lesson unfolds and how students respond (Bond et al., 2000).
Although not specifically mentioning automaticity, post-episode interviews of conductors in Bergee’s (2005) study appeared very similar to some of the descriptions and findings from other automaticity and expertise research. One novice conductor AUTOMATICITY EXPECTATIONS 71 mentioned she was too focused on the task, worried about her facial expressions, and thinking ahead for cues to attend simultaneously to other things. Another novice felt her actions were more reactive than proactive. Most of her concentration was directed toward execution of conducting patterns and cues. If she tried directing attention to either aspect more than the other, one would be lost. Overall, she reported being too focused on her own actions to be aware of what was going on in the ensemble. When an intermediate-level conductor was interviewed, he commented on the difficulty he had attending to details such as errors. He felt confident in his detection skills while not in front of the ensemble, but found it much harder when concurrently rehearsing and conducting. Finally, an expert conductor was interviewed. He believed he was at the stage where everything about the task was comfortable, as he was able to perform tasks together or separately with no difficulty. However, some of his comments were also in response to the performances of the other non-expert conductors. He felt the intermediate conductor had yet to reach the point where his gestures and thoughts could operate independently of each other. The expert conductor described the novices as coping through each moment and still giving themselves instructions about the steps and tasks that must be executed.
An expert teacher’s statement about what novices needed to know in order to become a successful educator also resembled the need for automaticity:
I think that what you have to learn is how to deal with mental jumbling. You
have to learn how to manage the . . . ‘mental mess’ provided by all the action
you see in the classroom and how to stay in control of yourself and the AUTOMATICITY EXPECTATIONS 72
situation in such a way that it continues to be a productive learning situation.
(Carter, 1987 as cited in Carter et al., 1988, p. 25)
Problem-solving skills. An important skill in any profession is the ability to solve problems. Sternberg and Horvath (1995) proposed experts differed from novices in their problem-solving abilities by how effectively they used their domain knowledge, the speed in which problems could be solved, and the likelihood that creative solutions could be found. Experts appear to be able to solve problems within their domain of expertise that novices are not able to, or are much faster and accurate in determining solutions. Richman, Gobert, Staszewski, and Simon (1996) found that when experts were asked to verbalize their problem-solving strategies, explanations were much briefer despite their more extensive skill, possibly indicating some tasks were automatic and therefore, performed without thought. Concerning problems often encountered within their domain, experts appeared to reach rapid solutions, but then were unable to communicate the individual steps they performed in solving the problem. Novice problem-solving strategies remained algorithmic, whereas experts could be prompted to produce a solution with a single cue.
Glaser and Chi (1988) provided two possible explanations for experts’ accelerated performance and problem-solving abilities. In order to develop expertise in their particular domain, individuals had to engage in much deliberate practice, which resulted in automaticity and increased mental capacity. Furthermore, experts were able to determine a solution to a problem without accessing as much of their memory because practice had enabled them to develop specific patterns of action that would occur as a result of certain prompts. AUTOMATICITY EXPECTATIONS 73
Bond et al. (2001) characterized expert teachers as having better strategies for solving problems, especially in their frequent assessment of hypotheses. Berliner
(2001, 2004) suggested expert teachers tended to be more sensitive to demands of the task and social situation when solving pedagogical problems. They represented problems in qualitatively different ways than novices by relying on personal sources of information and past experiences. This deeper understanding could involve understanding student success, recognizing patterns, reorganizing problem-solving strategies to be used concurrently with instruction, developing a collection of useful solutions, and being able to experiment with problem-solving strategies (Bond et al.,
2000). Berliner (1986) asserted that the problem-solving of expert teachers might be more complex than problem-solving in other domains of expertise because of the different types of knowledge (content, pedagogical, and pedagogical content) required.
Flexibility. Several sources mentioned the importance of flexibility in teaching. Berliner (2001, 2004) found that expert teachers were more flexible and able to take advantage of unexpected opportunities in the classroom context, such as teachable moments. Improvisation, educationally rather than musically, and the ability to adapt to the diversity of students were also viewed as necessary (Bond et al.,
2000). Similarly, Borko and Livingston (1989) found that expert teachers were able to use different teaching styles, accommodate for students while continuing to accomplish objectives of the lesson, and balance between content- and student- centered instruction. They were also able to comprehend different learning environments and adjust their teaching strategies appropriately (NBPTS, 2001). AUTOMATICITY EXPECTATIONS 74
Expert teachers possessed more pedagogical content knowledge, which could be used while teaching in flexible, new ways. Instruction could be improvised and changed according to the needs of the situation and learners. With increased knowledge of their students, expert teachers were able to anticipate problems learners would typically have with the content area and plan for these difficulties beforehand (Bond et al., 2000).
Westerman (1991) also found that flexibility increased with expertise in a comparative study of thinking and decision making at various stages of planning and teaching. Novice teachers constructed their lessons around specific objectives and did not make any modifications while teaching in order to address the needs of the students. Expert teachers, on the other hand, were able to consider learners’ perspectives while planning, as well as make necessary adjustments while teaching the lesson. Bransford et al. (1999), however, stated that all expert teachers did not possess the same amount of flexibility when approaching new situations.
Recognition of patterns. Experts in diverse domains share the capacity to quickly and accurately recognize meaningful patterns in their area of expertise
(Berliner, 1988, 2004; Bransford et al., 1999; Glaser & Chi, 1988; Patel et al., 1996), which can relate directly back to the development of schemata. This ability may assist in the reduction of cognitive load (Berliner, 1986). In educational contexts, expert teachers were able to notice patterns of classroom activity that might appear chaotic to novices (Hammerness et al., 2005). Furthermore, they were able to prioritize what was relevant over more trivial matters by filtering through incoming information and directing attention selectively (Bond et al., 2000; Chabris & Simon, 2010). Experts AUTOMATICITY EXPECTATIONS 75 also appear to recognize patterns faster, which may assist in noticing things that don’t correlate with prior knowledge or are typical and do not require as much attention.
This ability develops through much experience with the domain and is especially important to develop because of the limitations of conscious attention (Berliner,
1988).
Another characteristic of expert teachers was the improved competence in perceiving classroom events and student cues (Bond et al., 2000). Evidence of this was found in one experimental study: Expert teachers interpreted events with more detail and insight. They differed from novices in how they attended to simultaneous events and tasks in the classroom environment. Novices appeared to pay more attention to student behavior. Experts, while cognizant of the behavior, were not as critical and could infer possible reasons behind it, including causal relationships between student and teacher behavior (Sabers et al., 1991). Expert teachers in
Leicester’s (1990) study also exhibited pattern recognition abilities, which were usually demonstrated by the identification of typical student behavior and an extensive understanding of the interaction of students and teachers.
Analysis of expert teaching has long been a part of professional education at
all levels of instruction, but, even in light of seemingly endless discussions of
the definitions and indicators of good teaching, there remains considerable
debate about what makes an expert teacher. (Duke & Simmons, 2006, p.7)
The previous section attempted to categorize the many potential characteristics of expert teachers that have appeared in related literature. There appears to be no true consensus on what a good teacher looks like. Furthermore, if a AUTOMATICITY EXPECTATIONS 76 definition is agreed upon, the question remains: Where does the beginning teacher fall in relation to this? The following section focuses on the professional outcome of music teacher education, the beginning music educator.
The beginning music educator. While there has been much research on pre- service music teachers, Roulston, Legette, and Womack (2005) found that few studies looked at the transition of pre-service teachers into the teaching profession. It is commonly heard from beginning teachers that more is learned in the first few years of teaching than is learned throughout college (Haack, 2003). Likewise, teachers in
Roulston’s et al. (2005) study reported their experiences as first year teachers were difficult yet rewarding. Haack (2003) believed pre-service teachers would not be able to learn everything about education prior to entering the classroom. Similar statements were made by other authors: “The first couple of years of teaching are the most challenging because not even the best music education department, nor outstanding field experiences, can fully prepare the student for his first real classroom of students” (Mark & Madura, 2010, p.103); and “I [Conway] believe that even the best teacher education program cannot authentically prepare a beginning teacher for the reality of the first year of teaching” (Conway, 2003b, p.4). One current teacher interviewed by Roulston et al. (2005) also believed it would be impossible for her teacher education program to prepare her for all realities of teaching. No matter the quality of professors or preparation, some music educator skills would not be fully understood until they had to be personally performed with one’s own students.
AUTOMATICITY EXPECTATIONS 77
Knowledge and skills. In order to better prepare college students for the teaching profession, music teacher educators must contemplate what types of knowledge and related competencies are necessary for beginning teachers to succeed in their initial teaching experiences. The knowledge base of professional educators should be identified in order to illustrate the significance of teacher preparation and the years necessary to develop professional knowledge and skills. Furthermore, the identification of the profession’s unique knowledge base could contribute to the development of future teachers. Learning how to teach is unlike other forms of professional preparation in academia because there is less emphasis on “book knowledge” (Calderhead, 1991, p.533). Some examples of music educator knowledge and skills were outlined earlier in the literature review when the characteristics of expert teachers were discussed. The following paragraphs present additional information related to beginning music educators.
Beginning teachers planned their teaching, but mostly on a short-term basis.
They struggled with deciding what content should be prioritized. Furthermore, most of their planning was focused on the presentation of the lesson. Novice teachers admitted they were unable to predict where their students might have difficulty with the content. They also admitted rejecting certain teaching strategies because they did not feel ready to perform them yet. Beginning teachers did not seem as successful in carrying out their plans in the classroom and were sometimes unable to move the lesson forward if forced to respond to student questions. Concepts were often presented in a disjointed manner. Additionally, because of their limited prior knowledge, they had to develop, modify, or elaborate on the schemata they did have. AUTOMATICITY EXPECTATIONS 78
Once a scripted plan was developed, however, novices had difficulty deviating from it and improvising instruction. Similar challenges were reported in Reynolds’ (1995) literature review. Many studies reported that beginning teachers had trouble adapting their teaching materials and performance to student differences, establishing routines and rules in the classroom environment, understanding the subject sufficiently to explain it to students, and analyzing their own teaching performance (Borko &
Livingston, 1989).
Dewey (1933) believed the preparation of content knowledge needed to be more thorough than what was possible in any textbook or fixed lesson plan. Pre- service music educators had to develop the skill sets and knowledge for two different professional roles, the musician and the teacher, making their undergraduate preparation a unique discipline (Cutietta, 2007). In de-Leon-Baumann’s (2009) interviews of master music teachers, she concluded that teaching involved more than content knowledge. The development of many other abilities was also necessary, but these were often not taught to in undergraduate programs.
Pedagogical and pedagogical content knowledge are unique to the education profession but tend to be rather undeveloped in novice teachers (Borko & Livingston,
1989). However, perceptions of pre-service teachers’ pedagogical content skills appeared to be positively correlated with beginning music educators overall satisfaction in their professional preparation (Ballantyne & Packer, 2004).
Pedagogical content knowledge is most distinct between the expert and novice teacher (Shulman, 1987). However, Wilson, Floden, and Ferrini-Mundy (2001) AUTOMATICITY EXPECTATIONS 79 warned that strong claims about the importance of pedagogical content knowledge should be more cautious because Shulman’s concept was more hypothetical than fact.
Reynolds (1995) asked current teachers, teacher educators, school administrators, and other educational constituents what they believed beginning elementary teachers should be able to do when they enter the profession. Responses included: (a) the ability to perform most teaching tasks of their experienced colleagues, with the exception of budgeting and supervising other teachers;
(b) comprehension of pedagogy that contributes to teaching tasks; (c) content knowledge in traditional core subjects; and (d) the ability to read, write, listen, and communicate.
Pre-service and experienced teachers were asked to rank researcher-supplied abilities and behaviors important to successful beginning music teachers. The two populations agreed upon seven of the top ten rankings: (a) maturity and self-control,
(b) motivating skills, (c) leadership capacities, (d) involvement of student learners,
(e) confidence, (f) organization, and (f) optimism. Experienced teachers also differed considerably in their ranking of some teaching competencies when compared to their inexperienced counterparts. The experienced teachers placed much more importance on enthusiasm, on-task instructional time, maintenance of student behavior, and patience. In contrast, pre-service teachers ranked spontaneity and musicianship as more important skills for beginning music teachers (Teachout, 1997).
Some pedagogical content knowledge can be developed during teacher education courses, with additional progress occurring during pre-service practice in authentic contexts and initial professional experience. Teachers with pedagogical AUTOMATICITY EXPECTATIONS 80 content knowledge know more about their subject and how to best communicate it.
They also are better able to anticipate and diagnose common misunderstandings.
Firsthand student response is very informative to novices as they develop their teaching skills and knowledge (Grossman et al., 2005).
Challenges. Dewey (1904) discussed the challenges faced by beginning teachers, as well as the differences between experienced and novice teachers in the following statement:
The difficulties which face a beginning teacher, who is set down for the first
time before a class of from thirty to sixty children, in the responsibilities not
only of instruction, but of maintaining the required order in a room as a whole,
are most trying. It is almost impossible for an old teacher who has acquired
the requisite skill of doing two or three distinct things simultaneously – skill
to see the room as a whole while hearing one individual in one class recite, or
keeping the program of the day and, yes, of the week and of the month in the
fringe of consciousness while the work of the hour is in its center – it is almost
impossible for such a teacher to realize all the difficulties that confront the
average beginner. (p.13)
Pre-service teachers in Yourn’s (2000) study on teacher preparation communicated concern about their knowledge of classroom management and teaching materials, their performance as educators, their general knowledge of teaching, and expectations of them as novices in the profession. deLeon-Baumann’s
(2009) findings were similar. All master teachers in her study felt their undergraduate education failed to sufficiently prepare the classroom management abilities needed as AUTOMATICITY EXPECTATIONS 81 beginning teachers. Then again, beginning music teachers felt rather comfortable in most of their teaching competencies and responsibilities in a study by DeLorenzo
(1992) with only a few exceptions. Most of the teachers indicated difficulties in preparing a budget and continuing to grow musically following their undergraduate education. Secondary teachers were additionally apprehensive about their recruitment skills, while general music teachers and others who taught at multiple levels were concerned with content and curricular issues.
Beginning teachers’ perceptions of their first year teaching and the value of the preparation they received beforehand was the focus of Ryan’s et al. (1979) study.
The two themes that emerged included limitations of their teacher education and the importance of first hand experience during preparation. However, opinions concerning the adequacy of their education were inconsistent. The novice teachers in this study seemed to intuitively understand that their education would not be able to prepare them for all future possibilities. “My teacher training program was good, but so much more was needed that can’t be put into a course or a textbook. It is just being out there and doing. I felt as prepared as I could ever be” (p.269). Even with teachers who reported more extensive field experience in their preparation, a common belief was apparent that some realities of teaching could not be reproduced in teacher education. Furthermore, pre-service teachers’ experience in student teaching, while supplying in-depth knowledge and practice, had little in common with their first position. Results of the study included appeals for longer student teaching periods and the development of field experiences designed to enable pre-service teachers to experiment and practice with the knowledge being presented in coursework and AUTOMATICITY EXPECTATIONS 82 reading. Even at the time of this article, the balance between theory and practice in teacher education had been debated for quite some time.
Summary of section. The ways in which individuals become experts in their specific domain has been an important topic in recent cognitive science and psychological research. Experts have been differentiated from novices by their preparation, experience, and deliberate practice. Many studies, including those focused on educators, have compared expert and novice performance in order to better comprehend how expertise is developed. Some have viewed automaticity to be a characteristic of expertise. As opposed to more controlled tasks, skills that have become automatic have certain defining features, benefits, and limitations. For example, automatic processes do not diminish the capacity of working memory, can be carried out with minimal attention, and can often be performed concurrently with other tasks. There are some negative aspects of automaticity however, including choking, expert-induced amnesia, and inflexibility. In response to this final point, adaptive expertise may enable automatic skills to be used more flexibly in different situations, including the classroom.
Several sources emphasized the difficulty in narrowing down particular characteristics essential to expert music teachers. Some of the attributes that emerged upon analysis of the many articles on this topic included experience, personal traits, knowledge of students, and schemata. Remaining characteristics were divided into content, pedagogical, and pedagogical content knowledge, as well as categories related to the focus of this study: automaticity, problem-solving skills, flexibility, and pattern recognition. The preceding descriptors represent some of the desired AUTOMATICITY EXPECTATIONS 83 characteristics of expert teachers. However, other sources focused more on the beginning music educator, the knowledge and skills desired of this population, and the challenges faced by these individuals. A frequently seen opinion was the impossibility of developing all necessary knowledge and skills during undergraduate music teacher education. Some progress had to continue into the initial professional experiences of beginning teachers. On the other hand, by understanding the desired outcomes of beginning music educators, preparation may be improved in order to help these novices be successful. The following section continues with the framework of Backward Design by exploring what evidence indicates these desired results have been achieved. This section will also include information on possible evidence of automaticity from a neurological perspective and how the acquisition of teaching skills has been assessed.
Evidence
Knowledge and skill acquisition. “ . . . any useful conception of ‘expertise’ has to take into account the process of acquisition” (Bereiter & Scardamalia, 1993, p.4). There have traditionally been two types of skills in psychological research: psychomotor and cognitive. The former variety typically develops before most cognitive skills, thus appearing more primitive. Cognitive skills can be applied to a broader range of abilities, while psychomotor skills often have limited application to certain tasks. There also appears to be some evidence that these different types use different areas of the brain. Traditionally, the cerebral cortex is believed to control cognitive skills. This area of the brain distinguishes the human brain from other species. Psychomotor abilities, on the other hand, use areas of the cerebellum, which AUTOMATICITY EXPECTATIONS 84 are involved in visual, aural, and tactile perception of many organisms. Additionally, the skilled performer is better able to explain the knowledge behind cognitive tasks.
Psychomotor abilities are best acquired by physically executing the skill, rather than reading or hearing about it (Rosenbaum, Carlson, & Gilmore, 2001).
Fitts’ phases of skill learning, introduced in the first chapter (Fitts, 1964; Fitts
& Posner, 1967), may be applicable to both psychomotor and cognitive skill acquisition (Anderson, 1982; VanLehn, 1996). However, VanLehn (1996) believed the phases were an idealization. There are no distinct boundaries that separate one stage from the next. Additionally, when teaching cognitive skills, knowledge is divided into classes, topics, chapters, et cetera. Teachers may introduce an individual component and have the student practice it, and then immediately go on to another part while the student is continuing to work on acquisition of the original component.
Students may be simultaneously at different levels of acquisition for different portions of a skill. Despite his arguments, VanLehn recognized that Fitts’ distinctions were useful because of the observed details of each phase. As of 1996, however, most research had concentrated on the associative stage, in which skills are developed through practice, rather than the final phase dealing with automaticity.
Psychomotor skills. Two studies directly addressed the acquisition of psychomotor skills in connection with automaticity. Puttemans et al. (2005) proposed three stages of motor skill acquisition, very similar to Fitts’ phases of skill learning
(Fitts, 1964; Fitts & Posner, 1967). Much attention was demanded in the initial phase.
Performance levels became more established in the intermediate phase. The final stage was defined as the automatic phase. AUTOMATICITY EXPECTATIONS 85
Factors that appeared to influence how psychomotor skills were acquired were the focus of a literature review by Wulf, Shea, and Lewthwaite (2010):
• Observation was often dismissed as an ineffective method of motor skill
learning because it was not considered as effective as physical practice.
However, especially when combined with physical practice, acquisition
appeared to have been enhanced following observation. Some neuroimaging
studies have pointed to the possible existence of mirror neurons, in which
similar brain activation is apparent during both performance and observation
of an activity.
• Automaticity and efficiency of motor skills appeared to be more easily
developed when instructions were given to focus learner attention toward an
external outcome of a movement, rather than the movement itself.
• Feedback was essential as a motivational tool, encouraging learners to exert
effort toward acquiring the skill.
• There appeared to be evidence that the fourth factor, self-controlled practice,
was more useful than practice controlled by an external force (e.g., teacher,
coach). Active, rather than passive learning, was supported when the learner
was able to exercise some power. Self-controlled practice could possibly lead
to more learner involvement, intrinsic motivation, and a vested interest in
practice.
Cognitive skills. Cognitive skill acquisition has been defined as the attainment of problem-solving abilities in intellectual tasks. Successful acquisition is established by the learner’s knowledge, not necessarily observable behavior (VanLehn, 1996). A AUTOMATICITY EXPECTATIONS 86 substantial amount of time spent learning and practicing is essential to reach a proficient level on any significant cognitive ability. Anderson (1982) proposed a theoretical framework, based on Fitts’ phases of skill learning (Fitts, 1964; Fitts &
Posner, 1967), in order to explain how a cognitive skill develops and identify related learning processes. The initial, declarative stage involves the factual interpretation within the domain of the skill, which is similar to Fitts’ cognitive stage. This is followed by the second, procedural stage, in which knowledge is represented in procedures for executing the skill and is comparable to Fitts’ autonomous stage.
Anderson claimed proceduralization, referred to elsewhere as automatization, resulted in increased working memory capacity because information could be retained in long- term memory. Additionally, it became more possible for individuals to perform another task simultaneously.
In their Taxonomy of Skills Acquisition, Dreyfus and Dreyfus (1980) proposed that learners mentally passed through five stages when acquiring a skill. As progress was made, the learner relied on more abstract, rather than concrete, experiences. These developmental stages were labeled novice, competence, proficiency, expertise, and mastery. The novice learner was defined as a beginner who was provided with rules, monitoring, and feedback. Competence was achieved after much experience working in authentic contexts or learning to recognize meaningful patterns through the assistance of a teacher. Learners became proficient when they could understand the larger picture and were aware of details unnoticed by their competent peers while also being able to ignore unimportant information.
Expertise, the highest level, was believed to be reached when the learner accumulated AUTOMATICITY EXPECTATIONS 87 a large collection of situational knowledge, which immediately resulted in an intuitive response when activated. Mastery was a supplemental stage in which the expert became deeply engrossed in the performance of the task, arriving at a point where conscious attention was no longer needed, an idea very similar to the principle of automaticity. When considering the mental process of decision-making as part of certain skills, novice, competent, and proficient learners were analytical. However, experts and masters made decisions more intuitively.
The five stages proposed by Dreyfus and Dreyfus (1980) were also adapted to how teachers develop. Novice teachers, typically at the student teaching or first year level, deliberately followed rules. Real-world experience was crucial in their professional development at this stage. By the second or third year of teaching, or the advanced beginner stage, teachers were able to be more flexible in their application of rules. Teachers were able to plan and strategize in order to achieve goals at the competent stage. They were also less detached from the classroom because focus no longer had to be as egocentric. Additionally, competent teachers could recognize the relevancy of different things in the classroom context. Berliner (1988) theorized that talented or motivated teachers could achieve competency by the third or fourth year in the classroom. The final two stages were similar to automatic processing. The teacher functioned intuitively at the proficient stage by being able to know what must be done when confronted with certain circumstances, without needing to plan ahead.
In fact, those circumstances were often perceived unconsciously. Expert teachers only differed from the previous stage by their ability to respond appropriately and effortlessly. Decisions about what to focus on or carry out were believed to be no AUTOMATICITY EXPECTATIONS 88 longer consciously controlled or easily explainable by the teacher. While all previous stages were rational, expertise was believed to be more arational. Proficiency could be reached by the fifth year, but only some teachers ever transcended competency to this level. Even fewer ever truly became expert teachers (Berliner, 1988, 2001).
The five stages of this taxonomy were also applied to how conducting skills were acquired in Gaddis’s (1992) dissertation. University conducting teachers rated gestures and situations by their level of difficulty and then applied these samples to the different stages. Gaddis found that acquisition of psychomotor skills was generally considered easier and was affiliated with less advanced stages when compared to more expressive techniques or situations.
The descriptions of psychomotor and cognitive skills in the first paragraph of this section represented some of the initial understandings of these varieties. The conclusion of the review by Rosenbaum et al. (2001) indicated cognitive and psychomotor abilities might be more similar psychologically than once believed:
• Transferability might be possible in psychomotor abilities.
• There appeared to be more potential for creativity in psychomotor skills than
previously thought.
• The distinction that cognitive skills were more abstract while psychomotor
skills were reflexive no longer appeared to be as clear-cut.
• Neural activation for different types of skills might not be as specialized.
• Explicit and implicit knowledge were used by both varieties. AUTOMATICITY EXPECTATIONS 89
• While Fitts’ phases of skill learning initially applied to psychomotor skill
acquisition only (Fitts, 1976; Fitts & Posner, 1967), more recent research
supports the idea that these phases can also be applied to cognitive abilities.
Bloom’s revised taxonomy. In his original framework, Bloom (1956) attempted to improve learning, instruction, and assessment in education through a new method of organizing objectives. The revisions in the taxonomy represent more current perceptions of knowledge in cognitive science, psychology, and constructivism. According to Bloom’s revised taxonomy, knowledge is two- dimensional. The first dimension consists of the six cognitive processes, organized according to their complexity (remember, understand, apply, analyze, evaluate, and create). These have been slightly modified from Bloom’s original conception. The lower order abilities of remembering, understanding, and applying are most typically seen in educational objectives and are self-explanatory. The higher order skills involve understanding the components and structure, the ability to judge using criteria, and the capacity to generate something new based on lower levels of comprehension. The second dimension involves knowledge, which is organized on a scale from concrete to abstract (factual, conceptual, procedural, and metacognitive).
The factual variety is defined as knowledge of isolated facts and details. Conceptual knowledge is slightly less concrete because organization and relationships of knowledge is more comprehended. Procedural knowledge involves understanding how to perform tasks. The ultimate type is metacognitive knowledge, which allows one to be cognizant of one’s own thinking (Anderson et al., 2001). AUTOMATICITY EXPECTATIONS 90
Anderson’s et al. (2001) perspective is similar to expertise research in that knowledge is specialized according to domain and context. The varieties of knowledge in the second dimension have different connections to expertise performance. The vocabulary, symbols, and knowledge of domain-specific details of factual knowledge are relied upon for experts to communicate. Conceptual knowledge includes the development of schemata, which assist experts in pattern recognition. Procedural knowledge is related to routines and problem-solving. It also informs the individual about the proper time to use certain skills. The awareness of metacognition enables experts to utilize strategies, such as memorization tactics, across different domains.
Neurological basis of automaticity. Research on this topic has not been as extensive as neurological functioning while learning, possibly because of the more extensive time and resources necessary when studying automaticity (Ashby et al.,
2010). Neuroscience and cognitive science are making new discoveries about the human brain on a regular basis, making the individual findings appear rather disparate or contradictory at times. The following paragraphs report what is currently known about the brain in relation to automaticity.
When acquiring a new psychomotor skill, the development of automaticity is linked with changes in brain structure or functioning, known as neuroplasticity
(Debaere et al., 2004). Just as automaticity has been associated with a decreased need for attentive control of the performance, it has also been connected with decreased, or more efficient, neural activity in certain areas of the brain, in addition to increased activation in other areas. Neuroimaging was used to examine the shift in neural AUTOMATICITY EXPECTATIONS 91 activity as a psychomotor skill evolved from initial learning to an automatic stage.
Activation appeared to diminish overall as learning progressed. Researchers hypothesized that this was due to less attention being necessary in the performance of the automatic task. Such findings might provide evidence of the evolution from controlled to automatic performances, which is a characteristic of motor skill acquisition (Debaere et al., 2004; Puttemans et al., 2005).
Different forms of learning and memory appear to utilize unique neurological systems. Research on people with amnesia from traumatic brain injuries has helped prove the distinction between explicit and implicit memory. Explicit, or declarative, memory is the conscious recollection of specific episodes or facts. Implicit, or procedural, memory refers to more unconscious recall often used in performing skills.
Despite some evidence from participants in Salmon and Butters’ (1995) study, little was known about what was occurring neurologically in implicit learning and memory. However, new research has suggested there might be a connection between the neocortex and the basal ganglia. Looking specifically at the acquisition of psychomotor skills, the cerebellum seemed to have an important role in the timing and organizing of sequences. From the cerebellum, the sequence was stored in the basal ganglia. After much practice, the sequence developed into a central motor program underlying the task. Evidence in more recent cognitive science research has also pointed to the possibility that explicit and implicit processing rely on different brain structures. The working memory was believed to be housed in the prefrontal cortex, while cortical networks were involved with permanent storage. The consolidation of information occurred in the hippocampus. Explicit processing might AUTOMATICITY EXPECTATIONS 92 not be aware of knowledge stored implicitly, which could make it unavailable to working memory and conscious attention (Dietrich & Stoll, 2010).
Hocking (1999) studied automatization by comparing differences in neurological activation between new and automatic tasks. Response time decreased as practice increased, which was a possible sign of automaticity. The frontal activity seen when participants were initially learning a task suggested this area’s involvement in working memory. Decreased amplitude and increased latency during well-practiced skills could be suggestive of efficiency in neurological functioning, which was consistent with other findings.
A study of a concert pianist was recounted by Beilock (2010), which supplied further examples of the distinct roles within neurological functioning. The musician listened to a recording of her own performance and fingered along during magnetic resonance imaging. The prefrontal cortex, typically associated with the working memory and consciousness, appeared to not be as involved in the musician’s performance. Areas responsible for procedural, well-practiced memories, the sensory and motor regions, were more active.
In the past, new skills were thought to be processed in the cortex.
Automaticity was then developed by shifting control to subcortical structures. More current cognitive science evidence, however, suggested some subcortical structures, for example the striatum, also contributed much to initial learning. There were further indications that select neurons in the associative striatum were more active during early learning and less after extensive training, whereas neurons in the sensorimotor striatum displayed more activity after the development of automaticity (Ashby et al., AUTOMATICITY EXPECTATIONS 93
2010). Two parts of the brain believed to be involved in long-term memory, the putamen and the anterior cerebellum, also seemed to remain active throughout the process of developing automatic skills (Puttemans et al., 2005).
Departing from the larger components of the brain, some believe learning is biologically reflected in the neurons and synaptic connections of the brain. Synapses are thought to be a key to learning. The axons and dendrites that form the synaptic connections are strengthened by a fatty, protective substance that wraps around nerve fibers known as myelin. The more the connection is utilized (through repetition and practice), the more it is protected by myelin, making it more efficient, faster, and permanent (Coyle, 2009; Ormrod, 2008; Syed, 2010; Zull, 2010).
Myelination is also more likely to transpire when signals are sent from further distances in the body or are repeatedly used. This protects neurological connections from misfiring and increases the transmittable speed of signals between axons. It also stops neurons from branching into new synapses. New learning causes networks to create new branches and slow myelination. At birth, the cerebral cortex is almost completely unmyelinated, but begins the process of myelination as learning commences. It is currently believed that once neurons become myelinated, they are relatively permanent. The only exception would be certain demyelinating diseases
(Zull, 2011).
Coyle (2009) believed a new theory of skill acquisition could be developed if new research on myelin was combined with Ericsson’s research on deliberate practice. AUTOMATICITY EXPECTATIONS 94
Years of work go into myelinating a master coach’s [or teacher’s] circuitry,
which is a mysterious amalgam of technical knowledge, strategy, experience,
and practiced instinct ready to be put to instant use to locate and understand
where the students are and where they need to go. (Coyle, 2009, p.178-179)
Assessment. Teacher preparation programs are the most intensive effort to prepare music educators (Forsythe, Kinney, & Braun, 2007). “While most music educators agree that producing effective teachers is the ultimate goal of pre-service programs, there does not seem to be a consensus on what specific skills and characteristics should be taught and how they should be assessed” (Rohwer & Henry,
2004, p. 18). As can be seen in the previous section, there are many diverse opinions about what abilities are most important for teachers. Ballantyne and Packer (2004) surveyed beginning music teachers to investigate what knowledge and skills young professionals believed were necessary for successful teaching, as well as how these recent graduates perceived the effectiveness of current teacher preparation programs.
The findings of this study pointed to the need for increased support in the development of pedagogical and non-pedagogical skills. The authors concluded that teaching quality was directly related to the quality of teacher preparation.
The assessment of effective teaching skills in pre-service teachers is one of the most complex responsibilities of teacher educators (Hamann, Lineburgh, & Paul,
1998). Teaching competencies are typically assessed using evaluations within coursework (e.g., written tests, peer-teaching activities), field experiences, including student teaching, and informal observations of teaching (Applegate, 1985). One of the purposes of Rohwer and Henry’s (2004) survey of music education members of the AUTOMATICITY EXPECTATIONS 95
College Music Society (CMS) was to determine the most common methods of measuring teaching skills and personality characteristics. The most frequently cited forms of assessment were (a) in-class varieties, such as written tests and peer teaching exercises; (b) student teaching or field experiences; and (c) informal observations.
Methods of evaluating classroom preparedness, and its relation to future success, have also been identified. One can measure pre-service teachers’ skills in devising goals, learning outcomes, and lesson plans, in addition to how these teachers choose and arrange instructional materials. Assessment of presentation abilities has not been as straightforward. The prediction of future success in verbal and nonverbal communication and understanding is more problematic (Hamann et al., 1998).
The Survey of Teacher Effectiveness was designed to examine the lesson delivery, planning, and presentation competencies of music educators. Posture, eye contact, gestures, facial expression, and vocal inflection were rated using a Likert scale (1 = Poor, 5 = Excellent) in order to determine the effectiveness of lesson delivery skills. Subcategories in planning and presentation abilities included evidence of lesson planning (i.e., repertoire selection, the concept being developed, use of supportive materials, use of objectives, and organization of activities), subject matter knowledge (i.e., demonstrated in demonstrations, modeling, and conducting), pacing, sequencing of the rehearsal (including instructions and feedback), and teaching style
(Hamann & Baker, 2009). This instrument has been used in other studies (Austin &
Miksza, 2012; Butler, 2001; Fant, 1996; Paul et al., 2001), including an attempt by
Hamann et al. (1998) to ascertain whether any correlation was apparent between determined teacher effectiveness and social skills. Did certain social abilities predict AUTOMATICITY EXPECTATIONS 96 pre-service teacher effectiveness? Significant positive correlations were found between teacher effectiveness and emotional expressivity, emotional sensitivity, and social control. Within the survey’s second larger category of presentation skills, there was evidence of content, pedagogical, and pedagogical content knowledge. All assessment was based on observable behavior, but had no connection to this study’s focus on automaticity. Music educator skills and knowledge were represented to some extent but did not signify any achievement of automaticity.
Bergee (1992) attempted to create an evaluative tool for assessing music student teachers’ rehearsal effectiveness in order to replace the generic instrument often used in this more content-specific assessment. High school music teachers, music and music education professors, and music education graduate students ranked a collection of music educator skills according to their relevance for rehearsal effectiveness. The three larger categories that emerged were conducting technique, teacher-student rapport, and instructional abilities. Each had its own collection of ten sub-skills, achievement of which would contribute to that aspect of rehearsal effectiveness.
Professional training, not specific to teacher preparation, was evaluated in order to determine the attainment of objectives and whether the objectives could be successfully transferred over to work performance. Kraiger, Ford, and Salas (1993) developed a multidimensional system of classification for evaluating learning outcomes in order to create a model of training evaluation (See Figure 2.2).
Cognitive, psychomotor, and affective skills had subcategories, which were organized chronologically in the order of learners’ acquisition of outcomes. This model could be AUTOMATICITY EXPECTATIONS 97 linked to the focus of this study by the inclusion of automaticity in the skill-based outcomes category.
Figure 2.2
A Classification System for Evaluation of Learning Outcomes
Note: From Kraiger, K., Ford, J.K., & Salas, E. (1993). Application of cognitive, skill-based, and affective theories of learning outcomes to new methods of training evaluation. Journal of Applied Psychology, 78(2), 311-328.
Traditionally, the preceding skills were evaluated by observing the learner perform in simulations or actual job performance. In cognitive psychology, automaticity had traditionally been assessed through simulated tasks in experimental contexts, which might have been unrelated to how automaticity was used in authentic situations. In response to this disconnection between evaluation and reality, Kraiger et al. (1993) recommended three possible strategies for evaluating automaticity: AUTOMATICITY EXPECTATIONS 98
• Similar to dual-task research in cognitive psychology, trainees could execute
skills being developed while simultaneously performing an additional task.
Evaluators might conclude automaticity had been achieved when
performances of both skills has stabilized. When the trainee could
automatically process the initial task, their attention could be directed toward
improvement of the second skill.
• Evaluators could use a single task. The trainee would be asked to demonstrate
their use of the skill in solving typical problems and interference problems,
which would change some information from the standard problem. As
automaticity developed, the learner could chunk information into larger units,
but as a result, might not attend to certain important pieces of information. If
the performance degraded with interference problems, automaticity might be
inferred.
• The third strategy was also known as an embedded measurement. A skill
could be evaluated indirectly while the observer concentrated on another
aspect. Automaticity might be suggested when context and evaluation
methods were varied. For example, if no differences were evident in
performances within both a simple and complex context, the skill might be
automatic. In addition, if the trainee must no longer needed to consciously
attend to their own performance, automaticity might be assumed if the
performance deteriorated when they were asked to pay attention to specific
components of the skill. AUTOMATICITY EXPECTATIONS 99
Such approaches were part of the choking studies mentioned earlier in this literature review.
An additional study considered automaticity as a potential method of assessing cognitive skills. This was not meant to assess educational objectives, but could be adapted for such a purpose. However, in order for such a method to be most useful, researchers would have to: (a) develop norms of performance through cross- sectional analysis of students during various points in instruction, (b) create a database of successful and unsuccessful attempts at automaticity development, and
(c) monitor students longitudinally through the process of skill acquisition.
Researchers suggested certain aspects of performance changed throughout the development of expertise. The progress of competence could be documented as performance changes over time (Royer, Cisero, & Carlo, 1993).
At this time, there is no widely accepted measurement instrument to assess the acquisition of teaching skills among pre-service and beginning music educators, especially when contemplating the specific achievement of automaticity. Kagan
(1990) pointed out in her literature review that the idea of teacher cognition might be too large of a concept to utilize in the evaluation of teacher education programs and teachers:
• The idea of teacher cognition was rather vague in research, referring to:
(a) teachers’ thoughts while teaching and lesson planning; (b) teachers’
intrinsic beliefs concerning their pupils, the classroom environment, and
learning in general; (c) teachers’ self-reflections; (d) automatic routines; and
(e) self-awareness of how problems were solved. AUTOMATICITY EXPECTATIONS 100
• Teacher cognition could not be directly measured because it was often
unconscious, indescribable by the practitioner, possibly representative of
unpopular beliefs, and dependant on context.
• Assessment of teacher cognition was very time-consuming.
• It was difficult to compare cognition of different teachers.
The National Council on Teacher Quality (NCTQ) recently conducted a review of teacher preparation programs in order to determine the quality of student teaching experiences. The NCTQ’s standards for student teaching were compared to those of NCATE and the Association of Teacher Educators (ATE). Two of the
NCTQ’s assessment standards concerned how many times a university supervisor should observe the student teacher and how feedback should be delivered according to specified competencies immediately following observations. NCATE had no standard directly related to the scheduling of university supervision, except that formative and summative assessment should be continuously conducted with constant feedback. ATE, on the other hand, was more specific. Verbal and written feedback should be continuously given in formative and summative evaluations in order to inform student teachers about their progress in reaching specified outcomes. Weekly communication between the student teacher, cooperating teacher, and university supervisor should also be regularly scheduled. Moreover, student teacher performance should be evaluated in numerous ways (e.g., portfolios, self assessment, and peer assessment) (ATE, 2000; Greenberg, Pomerance, & Walsh, 2011; NCATE,
2008). AUTOMATICITY EXPECTATIONS 101
NCATE and ATE had other standards relevant to assessment and evaluation in teacher education. NCATE emphasized that decisions, which relied on pre-service teachers’ performance, should be based on several different types of assessments and occur at different points in their preparation (i.e., program admittance, transition stages, and conclusion of the program). The teacher education program should also investigate the validity and usefulness of assessment results on a regular basis in order to make adjustments. Ideally, pre-service teachers’ performance on such assessments would be strongly correlated to their progress in the program and their abilities after completing the program. Pre-service teachers should be evaluated continuously according to professional, state, and institutional standards. Numerous markers should also be utilized in predicting future professional success, such as grade point average, demonstrable ability in basic skills and content knowledge, and pedagogical knowledge (NCATE, 2008). A conclusion from the literature review accompanying ATE’s (2000) standards was that feedback and assessment were vital to quality field experiences. Increasing the number of field experiences would not improve teacher education if these elements were lacking. Evidence that pre-service teachers received regular, effective assessment and feedback from teacher education programs included:
• Supplying resources to teacher educators for providing feedback;
• Using consistent methods in evaluating readiness and progress;
• Planning frequent evaluative conferences between pre-service teachers,
cooperating teachers, and teacher educators;
• Employing different methods of feedback and assessment; AUTOMATICITY EXPECTATIONS 102
• Informing pre-service teachers about the expectations of field experiences;
and
• Continually developing knowledge, skills, and dispositions by pre-service
teachers.
Results of the NCTQ’s review of programs found that 48% of institutions required a university supervisor to evaluate student teachers at least five times during their practicum. During a typical semester, this resulted in supervisors going out to a school approximately every two to three weeks. This was determined to be an effective amount by Boyd et al. (2009). Some programs in this review mandated only two observations per semester. Thirty percent of programs did not have any evaluation requirements related to conferencing immediately after observations or giving written feedback. The researchers also reported inadequate measurement instruments when feedback did occur. Collected evidence of summative and formative evaluations used by cooperating teachers and university supervisors usually lacked organization and were inconsistent. Forms often did not explicitly state what student teachers were expected to achieve. Evaluation tools frequently did not provide an overall picture of the developing teacher (Greenberg et al., 2011). Assuming these results are analogous to the experiences of music student teachers, some serious issues in the assessment of pre-service teaching skills and knowledge are apparent, which could affect the professional performance of beginning teachers.
Summary of section. In order to determine whether desired outcomes have been achieved, there must be some type of evidence. It is also beneficial to understand how skills are acquired when outcomes involve specific cognitive or AUTOMATICITY EXPECTATIONS 103 psychomotor abilities. This was the focus of several psychological articles. Recent researchers used Fitts’ (1964; Fitts & Posner, 1967) phases of skill learning to broaden the comprehension of this topic. Others related skill acquisition, including teaching skills, to the development of expertise and mastery through different stages.
Evidence of automaticity has also started to appear in neuroscientific findings.
Activity seems to diminish with automatization, which suggests more efficient neurological functioning. There are also signs that diverse areas of the brain are involved with particular varieties of memory. Additionally, myelination might play a role in the neural efficiency associated with automaticity.
Assessment is a common strategy for documenting evidence of skill acquisition. However, just as there is little agreement about what skills are essential for music educators, there is no widely accepted method of assessing music educator abilities. Research points to evaluative tools that are frequently seen in coursework and beyond. Other measurement instruments specifically assessed lesson delivery, planning, presentation, and rehearsal effectiveness. No music educator assessments were specific to automaticity. However, potential strategies for measuring automaticity were included in a model of training evaluation. Additional sources discussed how pre-service teachers should be assessed during their undergraduate education. The upcoming section will provide a comprehensive review of the literature on music teacher preparation, including student teaching, as a means to develop the desired results discussed earlier in this literature review.
AUTOMATICITY EXPECTATIONS 104
Undergraduate Music Teacher Education
Curriculum. Many articles addressed the debate over what should be taught in music teacher education programs. Some of these were research studies, while others were more opinionated editorials. A prevalent topic was whether pre-service music teachers should be prepared as generalists or specialists. Should college students focus on one aspect of music education, or be prepared to function in any possible context? The answer to this question would inform the curriculum and experiences provided by institutions. Meske (1985) affirmed that decisions on music teacher education should be based on what professionals should ideally be doing in the classroom, rather than replicating what is currently being done. “Are we [music teacher educators] maintaining certain kinds of experiences and courses just because we’re victims of our own vicious cycle” (p.71)? Traditional teacher education programs are based on prior knowledge of schools and classrooms. Therefore, the novice teacher that is produced from such programs often resembles the current practitioner (Hollingsworth, 1989).
Schmidt (1989) surveyed the curricula of randomly selected undergraduate music education programs across the United States. Some music education course content seemed to be more prioritized because it was consistently identified as a requirement for music education students in various colleges, thus indicating a possible core of necessary knowledge. Over eighty percent of schools listed the following required topics in teacher education: lesson planning, grading, philosophy of music education, creative music activities, classroom management, student motivation, child development in music, and curriculum writing. Despite the frequent AUTOMATICITY EXPECTATIONS 105 appearance of these topics, Schmidt found that there was much variability in how much they were included in the curricula. The most common topics were unlikely to be the single focus of an individual course.
Rohwer and Henry (2004) asked music teacher educators what competencies were necessary in effective music teachers. In regard to teaching skills, classroom management was perceived as most important, followed by the ability to give clear instructions, control pacing, use eye contact, be organized, and use questioning. For music-specific teaching skills, the ability to be musically expressive was ranked as most important, above abilities in error diagnosis, sight-reading, knowledge of music theory and history, performance, conducting, singing, piano, and transposition. When considering instrumental music only, conducting and transposition were more important (ranked fourth and fifth) than they had been in the aggregate. Additionally, musical performance skills moved down from a rank of fifth most important for all music teaching areas to seventh in instrumental music.
The role of performance in music teacher education, in addition to expectations of performance abilities, has been widely debated over many years
(Trollinger, 2006). Reimer (2004) argued that performance studies in music education, both in the K-12 context and teacher preparation programs, were not essential to his reconception of National Standards:
. . . there is no reason to believe that highly developed performers make the
best general music teachers, just as there is no reason to believe the reverse.
We must get over the outdated and unrealistic assumption that performance is
the one, singular, royal road to being musical and being an effective music AUTOMATICITY EXPECTATIONS 106
educator. . . Teachers cannot be expected to do what performance specialists
do, and their teacher education curriculum must reflect that. (Reimer, 2004,
p.36)
Trollinger’s (2006) philosophical arguments diverged from Reimer. She contended that many of the knowledge and skills important to the successful music educator were also used by the performer. Trollinger called upon the profession to revisit the problem of producing beginning teachers who might not possess enough knowledge of their art form, or professional activities, to teach it. Wiggins (2007) also argued that pre-service music educators must become the best possible musicians if they were to develop the knowledge necessary to be the most effective teachers.
Berliner (1988) stressed that the main purposes of teacher education programs should be the preparation of the novice teacher and facilitation of the advanced beginning teacher’s achievement of competence. He also argued that competence should be the ultimate goal of teacher education. Pedagogical expertise should not be a professional aspiration for all teachers.
Specialists versus generalists. Cutietta (2007) criticized the preparation of music specialists in collegiate music education programs because of the continued assumption that there was no difference in knowledge or skills of the elementary general music teacher and the instrumental high school music teacher, that any music educator was qualified and capable to teach any musical subject. NBPTS (2001) made a similar statement concerning the unique challenges of music education.
Unlike many other school subjects, much variability existed in music teacher specializations and teaching assignments. On the other hand, many basic AUTOMATICITY EXPECTATIONS 107 competencies were identical. “ . . . although teaching general music and teaching performance ensembles are closely related in terms of the content knowledge required of the teacher, the instructional expertise required to teach in each context is distinctly different” (p.3).
The preparation of specialists (by genre, instrument, or level) was recommended in order to encourage the thorough development of knowledge in that specialization. Otherwise, rather than knowing an extensive amount of information on a single topic, one would know minimal information on many topics. Music teacher certification, which often qualifies educators to teach any musical subject, kindergarten through high school, is unrealistic (Cutietta, 2007). Berliner (2004) also argued that the idea of K-12 teacher certification was inappropriate when considering the preparation of expert teachers.
Cutietta (2007) believed it was impractical to expect collegiate programs to educate pre-service teachers to be considered high quality in every possible subject and level of music. By being trained as generalists, teachers have been forced to develop their music education specialization through personal experience, further education, and professional development. Colwell (2006) agreed that teacher education programs should prepare students for a specific ensemble or level. If additional expertise was necessary, teachers could enroll in graduate or summer classes. Even though music teachers often receive K-12 music certification or licensure, Colwell countered that teacher education programs should not contribute to the problem by continuing to prepare K-12 music educators. AUTOMATICITY EXPECTATIONS 108
Henry (2005) gathered information on state music education certification and licensure practices that were in existence as of the fall of 2001, which provides further substantiation to some of the concerns of Berliner, Colwell, and Cutietta. All- level certification transcended elementary, middle, and high school levels
(e.g., PK-12, K-12, 1-12). This was identified in 43 states. This was the only option for music teacher certification in approximately two-thirds of the states. Only in fifteen states was any distinction made between vocal and instrumental specialists.
Even fewer provided different certificates for vocal, instrumental, and general music teachers. With such wide distributions in levels and musical courses in most state certification, music educators may find they are responsible for teaching music beyond their collegiate specialization, thus supporting the argument for a generalist approach in music teacher education.
In Reimer’s (2004) proposed reconception of the National Standards, diverse musical roles and experiences were emphasized. In order to develop qualified general music specialists, teacher education programs would need to provide a broadened expertise to their pre-service teachers, reflective of his variation in the standards. In order to accomplish this, Reimer advocated for a separate general music education major, which would allow pre-service teachers to have more specific classes and experiences. Many requirements, especially those geared toward the instrumental music teacher, would not be relevant to the development of the general music teacher.
This idea strays from the generalist approach in music teacher education curricula, which assumes all pre-service teachers should have many of the same courses, despite AUTOMATICITY EXPECTATIONS 109 the fact that they may be preparing to be specifically band, orchestra, choir, or general music teachers.
NASM requirements. In order to fully understand the necessary competencies for beginning music educators, the accreditation requirements of teacher preparation programs must also be considered. NASM, the accrediting organization of collegiate music programs, has developed standards for music degrees including music education. According to its 2010 handbook, music education students were expected to develop the intelligence, abilities, ideas, and understandings necessary to be a musician. All music education programs, including all specializations, should include the following types of courses: conducting, arranging, performance, and historical analysis. More specifically, instrumental music educators were expected to have: (a) knowledge and basic performance skills for all families of instruments, sufficient to teach beginners; (b) experience performing in small and large ensembles; and (c) laboratory experience teaching beginning students in a variety of contexts. Observations and teaching experiences, ideally in actual school settings, were encouraged prior to official admission to the teacher education program
(NASM, 2010).
Forsythe et al. (2007) studied the opinion of teacher educators and pre-service teachers regarding the music education standards of NASM in order to clarify the importance of these standards for effective teaching to determine if they were achievable during teacher education. Two of the three most important competencies ranked by both populations were personal commitment and the ability to inspire others. Pre-service teachers also ranked conducting very high. However, this skill was AUTOMATICITY EXPECTATIONS 110 placed rather low among teacher educators. Despite their inclusion in the national standards, both improvisation and composition were ranked low in importance.
Concerning the learnability factor, teacher educators believed the most learnable teaching tasks were: (a) technical skills, (b) the knowledge of common elements and organizational patterns of music, and (c) sight-reading. Skills that were considered the least learnable were: (a) the ability to inspire others, (b) the ability to work in specific educational contexts, and (c) the understanding of relationships between musical professions and activities. It was assumed these skills would be acquired with teaching experience. When pre-service teachers were asked about the learnability of competencies, their top rankings were: (a) conducting, (b) music history knowledge, and (c) the ability to place music in historical, cultural, and stylistic contexts. The least learnable skills for this population were forming and defending value judgment about music and the ability to improvise or compose. It has often been assumed that the competencies included in the NASM handbook are embedded in music teacher education curricula of accredited music programs. However, the researchers warned that this had not been corroborated by research. It was difficult to determine the standards individual music teacher educators considered when developing the courses and experiences of their programs.
Opportunities for deliberate practice.
The paradox of learning a really new competence is this: that a student
cannot at first understand what he needs to learn, can learn it only by
educating himself, and can educate himself only by beginning to do what he
does not understand. (Schön, 1987, p.93) AUTOMATICITY EXPECTATIONS 111
It is difficult for pre-service teachers, with no prior personal knowledge of the realities of the classroom beyond the perspective of a student, to understand the relevance of much of the information presented in their undergraduate coursework.
For this reason, opportunities for deliberate practice in actual teaching situations can be very beneficial to pre-service teachers. Teacher education programs conclude with a student teaching period that usually includes extensive teaching practice. However, there are possibilities for more practice throughout pre-service teachers’ undergraduate years, such as field experiences and service learning programs. These can further assist the acquisition of automatic, expert teaching skills. The following section reviews studies on teaching experience and within teacher preparation programs.
“There is no substitute for experience when it comes to successful teaching”
(Mark & Madura, 2010, p.89). Simply providing pre-service teachers with general information about useful teaching strategies does not lead to deep understanding or the ability to perform those strategies (Hammerness et al., 2005). Berliner’s (2004) implications of considering expertise in relation to teaching included the importance of practice opportunities to improve acquisition of teaching abilities. One of the most effective ways to acquire knowledge and skills is to learn while performing activities
(Veenman, 1984). A first-year teacher in Ryan et al. (1979) also acknowledged: “The only way you can learn to teach is by teaching” (p.267).
Current music teachers rated actual teaching experience as the most valuable part of their teacher preparation (Ballantyne, 2007b; Bauer & Berg, 2001; Brophy,
2002; Conway, 2002; Teachout, 1997). Teachers often claimed they learned more AUTOMATICITY EXPECTATIONS 112 about teaching from their actual teaching experiences, rather than coursework
(Schmidt, 2010). More previous authentic teaching experience during undergraduate preparation was correlated with a higher quality of teaching performance during student teaching. Authentic-context learning activities were defined as those that presented problem-solving situations in an environment similar to actual classrooms.
The most obvious incorporation of these activities was in the student teaching period, but they could also be included within education coursework through early field experience opportunities, peer teaching exercises, and self-observation and reflection through video recording (Paul et al., 2001). Fant (1996) also reported a positive relationship between student teaching performance and early field experiences with feedback and microteaching exercises. However, the correlation became negative if feedback was not involved.
Conway’s (2002) qualitative study on the perceptions of recent music education graduates found that going out into the schools prior to student teaching was a beneficial part of teacher preparation. In order for performance-based teacher preparation to be effective, pre-service teachers had to have regular opportunities to practice teaching skills. Early and continuous field experiences were essential (Ester,
2004).
If practice provided pre-service teachers with information needed to successfully think and act as a teacher, the first year of teaching could be very difficult when beginning teachers had insufficient pre-service practice. Furthermore, if teacher preparation programs emphasized “book learning” rather than practice and reflection, novice teachers’ initial experiences in the classroom after graduation AUTOMATICITY EXPECTATIONS 113 would have more of an effect on their subsequent professional abilities. The authors emphasized it was crucial for pre-service teachers to have the opportunity to practice while still in their preparation program (Hammerness et al., 2005).
The need for deliberate practice in domains of expertise presents a problem when considering teacher expertise and the amount of practice included in teacher preparation. While there might be some proportion of field experience within coursework, the majority of deliberate practice occurs in the student teaching practicum. Beyond that period, teachers rarely deliberately practice their teaching skills during the rest of their career, at least if the typical concept of deliberate practice is applied. On-the-job performance provides experience, but not necessarily deliberate practice (Berliner, 2001). Ericsson et al. (1993) equated training with deliberate practice. Novices in many professions have a period of apprenticeship in which deliberate practice helps them develop an acceptable performance level. After completing this period, workers are expected to perform well, relying on practiced strategies rather than exploring new methods. During deliberate practice, repetitive experiences allow the novice to concentrate on certain aspects and improve upon seeing results or receiving feedback.
Considering Ericsson’s et al. (1993) definition of deliberate practice, Dunn and Shriner (1999) proposed ways in which current teachers could engage in deliberate practice. Ericsson et al. distinguished deliberate practice from actual work.
Therefore, the compilation of years of teaching experience would not be equivalent to deliberate practice. In order for a teaching activity to be considered deliberate practice, Dunn and Shriner proposed the following conditions: AUTOMATICITY EXPECTATIONS 114
• Teachers had to recognize that deliberate practice would improve their
teaching effectiveness.
• Much effort was necessary to begin and continue deliberate practice.
• Deliberate practice had to occur frequently.
• It should not be expected to be enjoyable.
Six activities were identified by teachers as the best examples of deliberate practice in teaching: (a) preparing materials for instruction; (b) mentally devising teaching strategies and classroom activities; (c) assessing student achievement through evaluation of work; (d) measuring student understanding informally; (e) engaging in formal, written lesson planning; and (f) determining student progress by creating and using written assessments. The authors recognized that the preceding activities, while considered typical among most teachers, were generally not regarded as deliberate practice. Despite the fact that most engage in these activities, it was believed many did not become competent teachers, and even fewer become experts.
Student teaching. The majority of deliberate practice in teacher education occurs in the period of student teaching at the end of the degree program. In studies reviewed by Wilson et al. (2001), both expert and novice teachers viewed student teaching as the most powerful part of their preparation. Standley and Madsen (1991) came to the same conclusion, with the addition of the cooperating teacher from the student teaching period also bearing much influence. When surveyed about their recent undergraduate preparation, beginning music teachers rated their actual teaching experience in student teaching and fieldwork as the most valuable feature of their program. Two participants commented: “I don’t think I really learned anything about AUTOMATICITY EXPECTATIONS 115 how to be a teacher until I hit student teaching” and “Student teaching is where it really all came together for me” (Conway, 2002, p.7). A beginning teacher in
Ballantyne’s (2007b) article made a similar statement: “Thank goodness for my practical experiences . . . I think more emphasis needs to be placed on the practicum process because this is really where most of the learning happens for the pre-service teacher” (p.125).
Teachers in Brophy’s (2002) study ranked the student teaching and methods components of their undergraduate education as most valuable. They also mentioned other types of teaching experience that were beneficial to their learning: (a) peer teaching, (b) field experiences through university coursework, and (c) self-arranged teaching experiences (giving private lessons, assisting in local schools, and teaching in a string project). Peer teaching was especially helpful in developing sequencing and planning skills. All but one of the participants felt their self-arranged teaching experience was more valuable than the field experience mandated by the university.
Brophy hypothesized that this might have been because self-arranged teaching provided more opportunities for “hands-on” experimentation and practice, more contact time with students, and more independence in the decision-making process.
Similar results were found in Hourigan and Scheib’s (2009) study. Student teachers believed they gained competency and understanding from many influential experiences and activities. Some were curricular, such as applied lessons, conducting class, ensembles, music education methods, and field experiences. Others were extracurricular, but were just as important, if not more so, to the development of teaching knowledge. These included freelance teaching, prior work experience, and AUTOMATICITY EXPECTATIONS 116 networking with other teachers. Student teachers, cooperating teachers, and music teacher educators in Brand’s (1982) article emphasized the importance of student teaching as a way to introduce pre-service teachers to the realities of the profession.
The student teaching period was relied upon to remedy idealistic ideas of young teachers and provided them with effective teaching skills prior to their first year of teaching. In contrast to the previously mentioned studies, Greenberg et al. (2011) concluded after an analysis of research studies on student teaching that some segment of the teacher education population did not view the student teaching practicum as essential to the development of effective first-year teachers.
Field experiences. The music teacher education curriculum may include many different music education methods courses prior to the ultimate student teaching period. Many of these courses traditionally incorporate field experiences (Reynolds &
Conway, 2003). The definition of field experience, as well as what opportunities it entails, varies within each program and sometimes, between different courses in the same program. Field experiences can also occur at different times in teacher education, depending on the program. Sometimes, they are connected with coursework. Other times, they are independent (Wilson et al., 2001). The basic concept of field experience may also be known by diverse names such as situated learning, immersion, internship, practicum, student teaching, practice teaching, and mock teaching. Moreover, many different activities could be incorporated into field experiences (e.g., peer teaching, laboratory ensembles, pre-service teaching, String
Projects, and teaching in laboratory or professional development schools) (Butler,
2001). AUTOMATICITY EXPECTATIONS 117
In a 2000 speech, Cutietta (as cited in McDowell, 2007) defined field experience was defined as any experience that took place in an actual school setting.
This could include observation, tutoring, mini-teaching, peer teaching, conducting, or doing instructional tasks. Field experiences could also be considered any “hands-on” task related to teaching but not actual instruction, such as operating media, planning, and designing materials. The most obvious type of field experience in teacher preparation was, of course, student teaching.
When looking at the literature of the time, Meske (1985) criticized the field experience concept, declaring that it was difficult to find a concise definition of what the field experience was designed to accomplish. More recent sources attempted to provide an explanation. Wilson et al. (2001) reported that there were different reasons for field experiences, including: (a) to show the realities of the teaching career, (b) to develop teaching and classroom management abilities, and (c) to connect the concepts learned in university coursework to the actual classroom. They were also designed to facilitate pre-service teacher application of knowledge and skills learned in college courses (Reynolds & Conway, 2003).
When current teachers were asked how much field experience, in comparison to coursework, should be included in music teacher education, most believed a 50/50 balance was ideal. Over 19% of teachers believed more field experience and less coursework were necessary (Brophy, 2002). Results of Schmidt’s (1989) survey of undergraduate music education programs presented a vast difference in the required number of field experience hours, ranging from zero to three hundred hours with an the average of approximately sixty-six hours. AUTOMATICITY EXPECTATIONS 118
Beginning teachers appeared to value components of pre-service preparation that were “hands-on” (Roulston et al., 2005). Hourigan and Scheib (2009) conducted a case study to determine student teachers’ value of field experiences prior to student teaching. All participants agreed that prior field experiences had a strong influence on the development of their teaching abilities. They had opportunities for observation, teaching, and conducting in five different music education courses and believed this allowed them to better apply the teaching methods they were learning to actual teaching contexts. A pre-service teacher in Schmidt’s (2010) study made a similar statement about the application of new teaching knowledge and skills: “. . . as soon as
I learned something, I could practice, come back to Practicum, learn something new, practice, come back to Practicum. So it was like a huge benefit, because it was like my own set of guinea pigs (p.137).
Early field experiences are often advocated because of the aspiration to make teacher education approximate reality. Denton (1982) explored to what extent early field experiences influenced the learning of pre-service teachers in future coursework.
Results of the study supported early inclusion of field experiences. Effects were moderate, but still more substantial than those observed for classes with integrated field experiences. Early field experiences prior to related coursework might supply pre-service teachers with contexts to which new knowledge can be related.
Criticisms. Field experiences have not been without their critics. They have been labeled as limited, disconnected from undergraduate education, and inconsistent.
They also were too focused on specific teaching skills and supported the current state of education and educators (Wilson et al., 2001). Fieldwork appeared to be only AUTOMATICITY EXPECTATIONS 119 useful when students were able to learn something specific from the context. If not organized properly, such as observations with no direction, pre-service teachers often did not value the experience (Conway, 2002). In Wilson and Floden’s (2003) addendum to their 2001 review of teacher education research, they admitted there was not enough research yet on what makes a high-quality field experience. Hitherto, it had not been proven how to create and offer field experiences that created better teachers.
In the late 1970s and early 1980s, there was a trend to increase the amount, variety, and extent of field experiences in teacher preparation. This trend assumed that when pre-service teachers were given the chance to observe learning and teaching and practice teaching skills, they would develop increased understanding and skill sets about students and teaching. Such beliefs presumed teaching abilities would be more effectively acquired in field experience situations, rather than in college coursework. This study examined the relationship between field placement characteristics and pre-service teachers’ abilities in these placements to their performance ratings at the end of their student teaching period. A surprising result, somewhat contradictory to previously mentioned studies (Fant, 1996; Paul et al.,
2001), was the weak correlation between cooperating teachers’ modeling of good practice and student teacher performance ratings. The authors supplied several possible explanations for this finding. Pre-service teachers might have failed to recognize their cooperating teachers as good models. Also, even though the cooperating teacher might have modeled successful strategies, there was no guarantee the student teacher would learn them. Lastly, either the student teachers or the AUTOMATICITY EXPECTATIONS 120 university supervisors might have viewed field experiences as practice opportunities for skills discussed in college coursework, rather than as a chance to observe and imitate successful teaching in an authentic context (Becher & Ade, 1982).
Field experiences have not always been viewed as a relevant part of teacher preparation. Zeichner (1980) investigated two popular myths regarding the value of field experiences in teacher education. The first myth was that better teachers would be developed when provided with experience in the schools. Criticisms at the time of this article included the opinion that field experiences were occurring in conservative environments, thereby creating new teachers who would merely repeat established practices. The second myth concerned the idea that teacher education institutions forced pre-service teachers to conform to conservative traditions and values. Besides addressing these myths, Zeichner sought to discover what was currently known about the impact of field experiences on the beliefs and practices of pre-service teachers, as well as what was actually learned in such experiences. This study produced two implications for teacher education. First and foremost, research at this time did not support the idea that field experiences in schools were valuable to the development of teachers. Increasing the number of field experiences would not automatically solve the existing problems in teacher preparation. Additionally, what pre-service teachers took away form field experiences did not always correspond to what had been intended.
The belief that all genuine education comes about through experience does not
mean all experiences are genuinely or equally educative. Experience and
education cannot be directly equated to each other. For some experiences are AUTOMATICITY EXPECTATIONS 121
miseducative. . . arresting or distorting the growth of future experience.
(Dewey, 1938, p.25)
The second implication was that field experiences were not as manipulative in forcing pre-service teachers into pre-existing molds as some critics thought. If teacher education programs wanted field experiences to effectively contribute to the development of teachers, the quality of these experiences and how they were put into practice had to be examined. Zeichner (1980) called for further research on what was actually learned from field experiences.
Service learning. Some believe service learning is “the antidote to what ails our nation’s education system” (Verducci & Pope, 2001). In music teacher education, the term usually refers to opportunities for pre-service teachers to provide instruction to students who would otherwise not have access to music. This type of program enables college students to directly apply the knowledge and abilities from their coursework in an authentic environment (Barnes, 2002; Reynolds & Conway, 2003).
Similarly, Reynolds (2004) defined service learning as a teaching and learning model that allowed pre-service teachers to gain knowledge through direct experience.
College students were able to apply knowledge and skills from their undergraduate coursework in an actual classroom while providing a service to the community.
Service learning is valuable for teacher preparation programs because it combines the application of knowledge and abilities, the realities of the classroom, and reflection
(Siebenaler, 2005). It also helps college students become socially integrated into the profession. They are able to start acting like a teacher and being treated as one, which AUTOMATICITY EXPECTATIONS 122 helps them begin to identify themselves with the role of the teacher (Burton &
Reynolds, 2009).
Community service should not be confused with internships. Community service, which lies at the heart of service learning, is intended to address the needs of the community. The most important focus should be serving others. Likewise, those who benefit most from the program should be those who receive the service.
Internships and field experiences typically focus on the learning of the pre-service teacher. Thus, this teacher benefits most from the situation. Service learning is a combination of community service and internships, focusing on and benefiting both the novice teacher and the community (Anderson & Hill, 2001).
An important component to service learning is reflection (Siebenaler, 2005).
In order to further facilitate pre-service teacher learning, structured reflection is included throughout the experience. Observation is also an important component
(Reynolds & Conway, 2003). Both of these distinguish field experience from pure volunteer work (Barnes, 2002). Service learning also provides teacher educators with the opportunity to assess pre-service teachers in more authentic situations than the peer-teaching context (Burton & Reynolds, 2009).
Verducci and Pope (2001) outlined why some teacher educators and programs have incorporated service learning into teacher preparation. Some of the most frequently heard reasons included:
• It was an effective pedagogical tool for teaching and learning. AUTOMATICITY EXPECTATIONS 123
• The university and surrounding communities could become more connected
through the cultivation of pre-service teachers’ social understanding, civic
participation, and social transformation.
• Both teachers and learners could benefit socially, morally, and personally.
• Pre-service teachers could be prepared to enter the profession through
authentic contexts.
• Teacher preparation could align with standards.
One teacher educator commented: “Our [pre-service teachers] have the opportunity to increase their subject matter knowledge, curricular knowledge, and pedagogical knowledge by participating in service-learning projects” (p.4).
The String Project is an ideal example of a program reflecting the values of service learning. The National String Project Consortium was originally developed by the American String Teachers Association in 1999 in order to provide teaching experiences for undergraduate string education students, offer low-cost orchestral instruction to children in the community, and combat the string teacher shortage (Byo
& Cassidy, 2005; Ferguson, 2003, Przygocki, 2009). By 2009, there were thirty-five universities with affiliated string projects, providing instruction to more than two thousand children and experience to more than three hundred future string teachers
(Przygocki, 2009).
String Projects are valuable because they offer an easily accessible teaching laboratory for pre-service teachers and teacher educators, allowing them to try new teaching approaches and immediately see the results (Przygocki, 2009). Two studies conducted research using the String Project context. Byo and Cassidy (2005) AUTOMATICITY EXPECTATIONS 124 surveyed the many populations involved in different String Project sites (e.g., administrators, master teachers, pre-service teachers, children, and parents) in order to evaluate the program’s effectiveness in encouraging college students to become string teachers, as well as stimulating growth in local school orchestra programs. Findings were inconclusive due to the short time the String Project program had been affiliated with teacher education institutions. The researchers believed the effects of the String
Project experience on the future string teaching population would emerge in the future.
Schmidt (2005) conducted a longitudinal qualitative study on the lesson planning of undergraduate pre-service teachers working in a String Project. The researcher was surprised by the results that, even after an entire year of authentic teaching experiences afforded by the university’s String Project, the participants did not have well-developed planning skills. She admitted she had unrealistic expectations concerning the pre-service teachers’ fluency of planning and teaching based on their limited teaching practice. Themes that emerged in Schmidt’s study included the perceptions that many decisions by pre-service teachers were made with little prior thought and little content from coursework had been transferred to teaching in the String Project. Her results suggest early and extensive supervised field experiences might be even more important than previously thought.
Effectiveness. The effectiveness of teacher education programs, in addition to suggestions for increasing effectiveness, was addressed in several studies. For example, the United States Department of Education published a summary of existing teacher preparation research. While there were a limited number of studies, those that AUTOMATICITY EXPECTATIONS 125 were reviewed appeared to indicate pre-service teachers were not being adequately prepared to teach high subject-matter standards. Novice teachers might have mastered basic teaching skills, but often did not have the necessary deeper understanding to sufficiently serve students (Wilson et al., 2001).
The problem of transferring knowledge gained during teacher education into the professional context was the topic of other studies. In-service teachers were mixed about the applicability of some coursework in undergraduate teacher preparation.
They did not feel they were able to connect the knowledge in coursework to the realities of their current position (Roulston et al., 2005). Undergraduate music education coursework was not reported as influential in the development of rehearsal techniques because they were often taught separately from actual teaching contexts. It was difficult for pre-service teachers to make the connection between their undergraduate curriculum and the actual classroom (Chaffin, 2009). Likewise, the results of Bauer and Berg’s (2001) research on influences on music educators showed undergraduate coursework had little impact on teacher planning, teaching, and assessment. The authors concluded that connections were not being made between the content of these courses and the realities of the profession.
Bauer and Berg (2001) explored what factors influenced planning, teaching, and assessment activities of high school instrumental music teachers. Required music education and music coursework was ranked relatively low in influence in all categories. The college ensemble conductor and applied teacher was viewed as much more influential than the university student teaching supervisor. The cooperating AUTOMATICITY EXPECTATIONS 126 teacher from the student teaching practicum was also relatively influential, especially compared to coursework and the university supervisor.
Brophy (2002) attempted to determine the effectiveness of music teacher education by polling current teachers on what national standards they felt most prepared to teach when first entering the classroom. Most reported they were prepared to teach the historical and cultural connections of music, as well as music reading and notation. However, less than half believed they were prepared to teach singing and interdisciplinary connections. Furthermore, only slightly more than half were confident in their ability to teach listening and instrumental skills.
The beliefs held by beginning music teachers concerning the effectiveness of their preparation were explored by Ballantyne (2006). Most did not believe they had been prepared sufficiently to confront the reality of the profession after completing their coursework. These teachers recommended more of an emphasis on pedagogical content and professional knowledge and skills. They also provided a list of topics that were deemed necessary when they entered the profession but had not been addressed enough in their preparation, including : running extracurricular activities, law issues, budgeting, and communicate with others in the school environment and community.
There has been little research on the relationship between teacher preparation and student outcomes on standardized tests. This may perhaps not be directly relatable to music education but is certainly representative of the current state of public education. Boyd et al. (2009) attempted to address this deficit while controlling for teacher and school characteristics. Field experiences during teacher preparation were one of several sources of information. One of the important results of this study AUTOMATICITY EXPECTATIONS 127 was teacher education programs connected to actual teaching practice were more likely to result in effective first-year teachers when measured according to student standardized test scores.
Preparation potential. Researchers’ opinions on the potential for teacher education to create effective beginning teachers were often not very optimistic.
Shulman (1987) believed there were so many viewpoints on what was necessary for effective teaching that he wondered how all those competencies and understandings could be developed during teacher preparation. One of the purposes behind beginning teaching, after completing a teacher preparation program, is the accumulation of experience. Berliner (1988) believed the profession should not expect more from teachers at this level. Bereiter and Scardamalia (1993) claimed there was no reliable way of producing expert teachers.
Music teacher education often bears the brunt of attacks regarding lack of
preparation for teaching. However, the problem areas identified in this study
(administrative duties, classroom management, parent interactions, and district
policies) are areas of beginning teacher concern that cannot be mastered until
arrival in a specific teaching context. (Conway, 2003a, p.21)
Recommendations for improvement. In Browning’s (2007) presentation on the development of expertise and its implications on music pedagogy and music teacher education, he suggested that expert-like thinking could be encouraged in pre- service music educators by challenging their prior content knowledge and perceptions about teaching through problem-based learning, self-observation and evaluation, and decision-making activities in authentic contexts. Referring back to Berliner’s (1988, AUTOMATICITY EXPECTATIONS 128
2001) model of teacher expertise, Browning (2007) noted teachers must navigate through each successive stage. Therefore, the typical procedures of competent teachers should be taught first. Novice teachers must master these procedures in order to eventually develop competency. Browning came to a similar conclusion as Feldon
(2007a) in his position that teachers at the advanced beginner and competent stages would reduce their cognitive load by being able to perform certain routines consistently. More mental capacity would be consequently available for problem- solving in the classroom. Future research on how expert teachers perform and think about their automatic routines might be helpful in articulating this knowledge to pre- service and novice teachers (Berliner, 1986).
Music teacher education curriculum should facilitate pre-service teachers’ development of all three types of knowledge (content, pedagogical, and pedagogical content). However, this is very difficult in programs consisting of disconnected coursework. For example, if music classes not specific to education (e.g., history, theory, performance) seem to be unrelated to the music education profession, pre- service teachers might fail to make the necessary connections with this knowledge that they will need in the future. Novice teachers might struggle with teaching historical or theoretical concepts to their future students, or even find it difficult to explain or model important performance skills. Wiggins (2007) suggested all faculty who work with pre-service teachers, even if they are not specifically music education professors, must work as a team and communicate what and how they teach to prospective educators. Furthermore, if the study of music education is associated with AUTOMATICITY EXPECTATIONS 129 how music is authentically made and learned, it may easier for other music faculty to relate to the efforts of the music education program.
In order to work toward the development of teacher expertise, as many classroom-based experiences as possible must be supplied to pre-service teachers.
Ballantyne (2007b) found that music teacher education courses needed to prepare pre- service teachers for those specific contexts in which they might work in the future and assist with transferability of knowledge and skills between coursework and the classroom. Furthermore, learning through observation should be emphasized and observation skills should be developed when pre-service teachers are not yet responsible for all the simultaneous tasks of professional teachers. Observations should not merely focus on the actions of the teacher, but also the social interactions between students and between teachers and students, as well as student moods and behaviors (Leicester, 1990).
Expert-induced amnesia, one of the potential limitations of expertise and automaticity, is the inability to explain the components of knowledge and skills that have become automatic. Therefore, this could be a potential problem in placing student teachers with expert cooperating teachers. “How can knowledge of expert teachers be made available to pre-service beginning teachers if such knowledge is, to a large degree, unarticulated, tacit in nature, and grounded in experience” (p.88)?
Ethell and McMeniman (2000) described a cognitive intervention of reflective practice and situated cognition, intended to address this concern. The authors defined situated cognition as a method to make implicit, unarticulated knowledge of expert teachers more explicit and communicable to pre-service teachers. Such a method AUTOMATICITY EXPECTATIONS 130 would enable novice teachers to access the cognition of expert teachers. Indeed, findings supported the pre-service teachers’ comprehension of the connection between theory and practice.
A problem observed in the training of military personnel could be applicable to the preparation of teachers. Holt and Rainey (2002) theorized that the lack of adaptive thinking in military leaders was not a result of insufficient knowledge, but of a deficiency in realistic situations that allowed for application of knowledge. As a result, some abilities remained cognitively demanding. Providing more opportunities for deliberate practice, rather than additional knowledge, could further develop adaptive thinking.
This is similar to Brophy’s (2002) previously mentioned finding that most teacher educators desired either an equal or larger ratio of field experience to coursework. Brophy also asked music educators to supply recommendations for improving student teaching. Most suggested additional field experiences and pedagogical courses prior to student teaching. Far fewer teachers proposed more coursework to improve general musicianship and methods instruction from a teacher who continued to work with children.
In November, 2010, NCATE released a recommendation from a group of education experts: More hands-on, clinical instruction, akin to the preparation of medical doctors, needed to be incorporated into teacher education. The current method of teacher preparation was considered ineffective because of its overemphasis on coursework. Significantly more authentic practice and feedback throughout pre- service teachers’ college preparation, not merely during student teaching, was AUTOMATICITY EXPECTATIONS 131 recommended. The group suggested teacher education programs be redesigned in order to incorporate authentic practice with content of coursework. They also proposed more involvement of experienced teachers as mentors and clinical instructors. Similar to some of the recommendations directed toward K-12 education, the panel also suggested: (a) holding teacher education programs more accountable;
(b) improving pre-service teacher recruitment based on academics and desired educational attributes; (c) matching placement of student teachers with the needs of cooperating school districts; (d) building stronger partnerships between teacher education programs, local governments, and school districts; and (e) collecting more evidence about successful teacher education programs (Kiley, 2010).
Teacher expertise research has provided several implications for teacher education:
• Deficiencies in observing and student teaching, at times characteristic of
alternative teacher education programs, might interfere with novice teachers’
abilities to attain competency or expertise, or even endure the first few years
in the classroom.
• Mentoring appeared to be relevant in the acquisition of teaching abilities and
the reduction in attrition of novice teachers.
• Contextual opportunities to practice teaching skills were very beneficial in
developing teaching skills.
• The concept of K-12 certification might be incompatible with the intent of
teacher education to develop teacher expertise. AUTOMATICITY EXPECTATIONS 132
• A limitation of expertise was the domain in which the individual could
expertly function. Therefore, an expert in one musical subject would likely not
find the same level of success when teaching another musical subject or level.
• Contextualized knowledge might be difficult to generalize or transfer. For
example, a written test may be a less valid way of assessing teaching
competencies, in comparison to observing the teacher in an authentic context
(Berliner, 2004).
Summary of section. This final portion of the literature review discussed the instructional experiences in undergraduate music teacher education that are responsible for creating the desired results and evidence of the preceding sections.
Several researchers focused on what topics or skills should be included in the curricula of undergraduate music teacher education programs. The debate over whether music educators should be prepared to teach all musical subjects or a specialization was addressed in other studies. Research indicates deliberate practice is essential to the development of expertise and automaticity. Student teaching, field experiences, and service learning are often included in teacher education because of the practice opportunities they provide. Many pre-service teachers, beginning music educators, and researchers ranked actual teaching experience as the most valuable part of teacher preparation. However, there have also been criticisms of these experiences, such as their consistency, relationship to the overall curriculum, focus, and relevancy. Furthermore, music education researchers have studied the effectiveness and potential of undergraduate music teacher education programs to develop beginning music educators. AUTOMATICITY EXPECTATIONS 133
Summary of Chapter
Experts in a variety of domains are often distinguished from novices because of their preparation, experience, and deliberate practice. Teacher expertise and educational researchers have reported similar distinctions and have attempted to further identify characteristics shared by expert pedagogues. However, many have found it difficult to reduce possible traits down to a list shared by all expert teachers.
Potential patterns observed in articles of this literature review included experience, personal traits, knowledge of students, and schemata. However, many other characteristics cited in these articles did not fit as neatly into larger categories. They were subsequently organized into Shulman's (1986, 1987) varieties of teacher knowledge and attributes representative of automaticity and expertise research. These characteristics are included in the "Desired Results" section of the literature review, not because they are necessarily expected at the conclusion of music educators' undergraduate preparation, but because they represent eventual professional outcomes. Many music education researchers have examined the beginning music educator population, how prepared they are upon entering the profession, what knowledge and skills they have developed, and what challenges they face.
Psychological researchers have considered the acquisition of psychomotor and cognitive skills, both of which are needed by music educators. Many studies incorporated Fitts’ (1964; Fitts & Posner, 1967) three phases of skill learning, while others proposed different multiple stage theories. The field of neuroscience has also contributed to the understanding of skill acquisition automaticity. Decreases in neural AUTOMATICITY EXPECTATIONS 134 activation, utilization of certain areas of the brain, and myelination suggest potential neurological evidence of automaticity.
Music teacher education research has yet to confirm automatic music educator skills through existing measurement instruments. However, it also does not possess a widely used means of assessment pertaining to music educator skills in general. Some standardization does appear in undergraduate music teacher education programs because of requirements of accrediting organizations such as NASM. Nevertheless, the content, effectiveness, and relevancy of aspects of undergraduate preparation are still often debated in music education research.
The purpose of this study was to examine instrumental music teacher educators’ perceptions regarding the level of automaticity of teaching knowledge and skill expected of beginning instrumental music teachers. While it is unlikely all abilities can be completely automated during undergraduate preparation, it is hypothesized that automaticity is possible for some music educator skills and that they can be sufficiently developed in teacher education through deliberate practice opportunities in undergraduate curricula. Those skills that have yet to reach an automatic level may be approaching this stage, thus enabling them to become automatic at a faster rate in the first few years of teaching, especially compared to other skills that received no practice or attention during teacher preparation. If beginning music educators have a collection of accessible, well-practiced skills, they will be able to direct their attention where necessary in the classroom and potentially avoid cognitive overload, teacher burnout, and professional attrition. There is currently no widely accepted agreement on what an undergraduate music education AUTOMATICITY EXPECTATIONS 135 student should look like upon graduation. There is also no codified consensus on what music educator skills should be automatic when beginning teachers enter the profession, or how to monitor the acquisition of these abilities. There is a growing body of automaticity research, but this has yet to be tied to music teacher education.
This study attempts to develop this connection.
AUTOMATICITY EXPECTATIONS 136
CHAPTER 3
Methodology
The purpose of this study was to examine instrumental music teacher educators’ perceptions regarding the level of automaticity of teaching knowledge and skill expected of beginning instrumental music teachers. The following research questions were explored:
• How were music teacher education programs preparing the music educator
skill set necessary for beginning instrumental music teachers?
• How were music teacher education programs evaluating the music educator
skill set necessary for beginning instrumental music teachers?
• What teaching skills did music teacher education programs expect to be
automatic in their graduating instrumental music teachers, as reported by
music teacher educators?
• What performance skills did music teacher education programs expect to be
automatic in their graduating instrumental music teachers, as reported by
music teacher educators?
This study represented a continuation of the author’s previous qualitative research on automaticity expectations in string teacher education. In the preceding study, string teacher education approaches were compared by interviewing teacher educators of a standard university music education program, a Suzuki teacher training program, and a university program with an affiliated String Project. This study sought to understand the unique ways pre-service teachers were being prepared, the similarities and differences in degree requirements and field experience opportunities, AUTOMATICITY EXPECTATIONS 137 and teacher educators’ expectations for automatic music educator skills in their graduates (Peterson, n.d.). The current study quantitatively obtained more detailed information on preparation, evaluation, and automaticity expectations from a larger population of instrumental music teacher educators.
Participants
Collegiate instrumental music teacher educators were surveyed for this study.
Participants were faculty identified in the 2010-2011 College Music Society (CMS) directory (CMS, 2010) that also met the following criteria:
• Qualified as a music teacher educator (taught music education coursework,
arranged student teaching placements, collaborated with a cooperating
teacher, and/or observed student teachers through the final practicum period
of undergraduate teacher preparation (Ely & Rashkin, 2005));
• Were involved in the preparation of instrumental music teachers (band and
orchestra);
• Currently taught in the United States (CMS membership is open to music
faculty in Canada and Puerto Rico.);
• Possessed an obtainable email address;
• Did not participate in the pilot study; and
• Were not on the music education faculty at the researcher’s institution.
Eight hundred and sixty-seven members of the College Music Society were initially invited to participate in the study. Emails to six individuals immediately bounced back, possibly indicating the address was no longer active or the instrumental music teacher educator was no longer associated with that specific AUTOMATICITY EXPECTATIONS 138 institution. Twenty-two members contacted the researcher, providing the following reasons why they should not be included in this study’s population:
• They no longer taught any music teacher education courses;
• They did not teach any classes in instrumental music education, specializing
more in vocal or general music;
• They did not teach any undergraduate music education courses;
• They currently held an administrative position and therefore, no longer taught
any music education courses;
• They believed they did not possess sufficient knowledge of the specific
information sought in the survey;
• They were on professional leave or sabbatical; or
• They had retired.
Any of the preceding reasons, in addition to concerns about security and length of the instrument, might have explained why other individuals failed to respond or complete the survey.
Of the 22 individuals who contacted the researcher, eight recommended another faculty member at their institution. The researcher subsequently emailed an invitation to these music teacher educators. Two additional surveys were completed but excluded from the final results: The first respondent appeared to fill out the survey from a vocal music education perspective. The other responses did not work with pre-service music teachers through their student teaching and certification because their community college only provided transferable credits to a different institution. AUTOMATICITY EXPECTATIONS 139
Considering all the previous factors, the new potential number of respondents was 845. Three hundred and three music teacher educators participated in the study, which represented a 36% response rate. (Due to the necessary statistical analysis, respondents who failed to complete at least half of the Automaticity Expectations section of the survey were omitted. Seventy-nine surveys were accessed but remained incomplete.)
Participant experience as music teacher educators ranged from one to thirty- five years (M = 15.88, SD = 9.82). Pre-school through high school teaching experience prior to becoming a music teacher educator varied between zero and thirty-five years, averaging 10.31 years (SD = 7.83). Approximately 78% of the respondents indicated they taught several types of classes or private lessons during their school teaching experience (266 band, 127 general music, 102 orchestra, and 91 choir). Additionally, 163 taught private lessons. Some music teacher educators taught other courses beyond the typical ensemble or general music classes during their pre- school through high school experience. (see Table 3.1).
AUTOMATICITY EXPECTATIONS 140
Table 3.1
Other PK-12 Teaching Experience by Participants
Courses n Music Theory 26 Jazz 15 Guitar 12 Theatre (i.e., Musical Theatre, Pit Orchestra, Technical Theatre) 8 Piano (i.e., Group piano, Keyboard, Piano) 5 Music Appreciation 5 Technology (i.e., Electronic Lab, Music Technology, Technology) 5 Composition 2 Music History 2 Percussion Ensemble 2 Adaptive Music 1 Classroom General Music 1 English 1 European History 1 Fine Arts Survey 1 Head Start 1 Humanities 1 Math 1 Popular Music Performance 1 Pre-K Music & Classroom 1 Rock Music Elective 1 Special Education 1
Two hundred and ninety-five instrumental music teacher educators (97.4%) earned some type of undergraduate degree. (The remaining eight who did not indicate a degree probably also had some type because they held a master or doctoral degree.)
Nineteen instrumental music teacher educators (6%) had more than one undergraduate degree. Two hundred and ninety-six individuals (97.7%) reported master degrees. (This answer was skipped by seven participants, but they also likely held a graduate degree as a prerequisite to their reported doctorate.) Eighteen participants (59.9%) indicated more than one master degree. Doctoral degrees
(including teacher educators who had completed their coursework, but not their AUTOMATICITY EXPECTATIONS 141 dissertation at the time of this study) were held by 89.1 % of the teacher educators
(n = 270). Five participants appeared to not hold any type of degree related to education (e.g., Administration & Supervision, Administrative Certification, Arts in
Educational Administration, Curriculum & Instruction, Diploma of Fine Arts in
Kodaly, Education, Educational Administration, Education Leadership, Education
Specialist, Music Education, Performance and Pedagogy, and Teaching). However, two of these teacher educators might have held an earlier degree in education, but only indicated their non-educational doctoral degree. Music education degrees were, overall, most frequently reported for each level, followed closely by performance and conducting. Table 3.2 illustrates the frequency and variety of different degrees conveyed by participants. Music education, performance, and conducting appeared to be the most common degrees of music teacher educators in the current study.
AUTOMATICITY EXPECTATIONS 142
Table 3.2
Frequency of Instrumental Music Teacher Educator Degrees
Bachelor Degrees n Master Degrees n Doctoral Degrees n Music Education 274 Music Education 174 Music Education* 188
Performance 24 Performance 64 Conducting** 51
Music 4 Conducting 47 Performance 17
Business Management 1 Educational 5 Education 4 Administration Church Music/ 1 Performance & 5 Curriculum & 2 Education Pedagogy Instruction Combined Education 1 Education 4 Administration & 1 & Performance Degree Supervision Education 1 Music 3 Administrative 1 Certification English 1 Composition 2 Artist Diploma 1
Jazz Studies 1 Curriculum & 2 Diploma of Fine Arts in 1 Instruction Kodaly Liberal Arts 1 Music History 2 Education Leadership 1
Math 1 Music Theory 2 Education/Music 1 Education/Curriculum & Instruction Music Theory 1 Arts in Educational 1 Education Specialist/ 1 Administration Education Leadership Performance 1 Ethnomusicology 1 Education 1 Certificate Specialist/Music Theory/Composition 1 MA in Pastoral 1 Music Composition 1 Ministry Music History/ 1 Musicology 1 Literature Music Literature/ 1 Musicology/Ethno- 1 Performance musicology Musicology 1 Performance & 1 Pedagogy Teaching 1
Note. * Includes three music teacher educators who had not completed their dissertation yet. ** Includes one music teacher educator who had not completed their dissertation yet.
In their current positions, the most common rank was associate professor
(n = 99), followed by 86 professors, 74 assistant professors, 18 adjunct instructors, 11 lecturers, 4 professor emeriti, 3 academic staff, and 2 visiting assistant professors.
Some individuals indicated other professional ranks (i.e., Director of Athletic Bands; AUTOMATICITY EXPECTATIONS 143
Director of Instrumental Music; Full-time, non-tenure, Instrumental Music Education;
Instructor (teaching faculty), No ranking at their respective university; and Visiting
Instructor). The most prevalent professional duty of participants was music teacher educator (n = 288), which might have involved teaching foundational coursework, methods, technique classes, conducting, and/or other coursework in the music education program. Other common roles included university supervisor (n = 211), academic advisor (n = 190), ensemble conductor (n = 150), researcher (n = 134), administrator (n = 107), applied lessons instructor (n = 71), and performer (n = 65).
Some music teacher educators listed additional teaching responsibilities for music and non-music major students beyond those supplied by the researcher (i.e., music theory and related topics, graduate classes, music for education students, music history, music education committee, String Project master teacher tasks, clinical experience, introduction to creativity, music appreciation, music in general studies, cross-cultural experiences, chamber music, string pedagogy and literature for performance students, and psychology of music). Participants taught at different types of institutions (198 public, 98 private, 4 conservatory, 2 community college). Two music teacher educators were currently employed by both a public and private institution.
Data Collection Instrument
Data were gathered through an online survey instrument using
SurveyMonkeyTM. The general purpose of survey research is to quantify trends, attitudes, or opinions of a population. This type of research design was chosen because of its ability to reach a large number of participants quickly (Creswell, 2009;
Leedy & Ormrod, 2010). Requests for participation and links to the survey were AUTOMATICITY EXPECTATIONS 144 emailed to each individual (see Appendix C). Reminder emails were sent to the population two weeks after the initial email, as well as five days before the survey was scheduled to close (see Appendices D and E). A consent form appeared at the beginning of each survey in order to satisfy Institutional Review Board protocols.
Survey questions included Likert-type scales, single and multiple response questions, and opportunities for short answers.
The survey instrument designed for this study consisted of three sections (see
Appendix B). At the beginning of the survey, participants were asked to supply demographic information on their teaching experience, educational background, and current position. Data on music education programs were then requested in the second section in order to obtain data on each institution. The final section of the survey concerned the automaticity expectations for skills of beginning instrumental music teachers who had completed undergraduate instrumental music education degrees at participants’ institutions. This section was based on a preliminary task analysis of instrumental music educator skills developed by the researcher (see
Appendices A). Because core music teacher education curricula are somewhat codified through professional organizations such as NASM and InTASC, these were considered collectively in conjunction with the related research literature on music teacher preparation and music educator skills. The individual tasks in this analysis appeared in the automaticity expectation section of this survey (Questions 24 through
37), but only represent some of the numerous competencies instrumental music educators must develop. Music educator skills were organized into the following larger categories: AUTOMATICITY EXPECTATIONS 145
• Performance and teaching skills related to music theory knowledge;
• Performance and teaching skills related to music history knowledge;
• Performance and teaching skills on a primary instrument;
• Performance and teaching skills on secondary instruments (i.e., brass,
woodwind, percussion, strings, guitar, harp, and recorder);
• Performance skills on voice;
• Performance skills on piano;
• Conducting skills in an instrumental ensemble;
• Classroom management skills;
• General rehearsal skills or techniques; and
• Technological skills.
Some incorporated competencies were not necessarily associated with the typical responsibilities of band and orchestra teachers, but were rather more regularly used in vocal or general music instruction (e.g., voice, recorder, guitar, and piano).
These were included because most beginning instrumental music education teachers receive a K-12 music certification or licensure upon completion of their undergraduate program (Henry, 2005), thus indicating they should also be qualified to teach other courses beyond band or orchestra. Furthermore, some skills, such as vocal modeling and piano proficiency, were often needed for instructional strategies and responsibilities of instrumental music teachers.
Music teacher educators were asked to rate the level of skill acquisition of typical beginning instrumental music teachers upon completion of their institution’s undergraduate music education degree. Options denoted degrees of automaticity: AUTOMATICITY EXPECTATIONS 146
• 1 = The beginning teacher MUST completely focus on this skill while
performing it (Nonautomatic);
• 2 = The beginning teacher can perform the skill while OCCASIONALLY
focusing on something else (Beginning Automatic);
• 3 = The beginning teacher can perform the skill while MOSTLY paying
attention to something else (Approaching Automatic); and
• 4 = The beginning teacher can perform the skill while TOTALLY paying
attention to something else (Automatic).
If respondents indicated a music educator skill was automatic to some extent, the type of automaticity assumed by the researcher was intended and goal-dependent.
Bargh (1989) defined this variety within the triple mode view by the following characteristics:
• The individual was aware of the stimulus in the environment that prompted
use of the skill.
• The individual had a specific goal in mind, for which the skill was being used.
• The individual consciously intended for the skill to occur.
• The skill did not need to be the total focus of the individual’s attention.
• The individual did not have to be conscious of the skill in order for it to be
completed.
Pilot Study
To finalize the preparation of the survey instrument, develop reliability, and establish content validity, the researcher conducted a pilot study of music teacher educators and music education graduate students with teacher preparation AUTOMATICITY EXPECTATIONS 147 responsibilities (N = 20). Participants were asked to provide suggestions for improvement on the instrument’s wording, construction, length, and clarity. (These individuals were subsequently excluded from the study’s population.) The response rate for the pilot study was 50%. Results and comments informed some adjustments to the survey instrument, including the elimination of required answers, combining secondary instrument questions to avoid redundancy, adding “unknown” options for many of the curricular and automaticity expectation questions to accommodate for possible limitations in participant perspectives, and incorporating more concise questions about evaluative strategies currently in use by music education programs.
Formatting corrections, with more frequent page breaks and standardization of Likert- type scales, were also made in order to increase the usability of the instrument.
Data Analysis Procedures
Descriptive statistics were used to describe the similarities and differences between the study’s participants, the characteristics of the many represented music teacher education programs, and the participants’ expectations. Automaticity expectations were further analyzed for potential correlations between characteristics of programs or participants and reported expectations using Pearson Product Moment
Correlations. Statistical analysis was conducted on both individual skills and categories, in addition to cross-categorical skills such as error detection, error correction, modeling, sequencing skills for beginners, communication, and adaptive expertise. Table 3.3 documents how this study’s research questions were reflected in the survey instrument. Demographic information on participants and programs went AUTOMATICITY EXPECTATIONS 148 beyond the four research questions, but were utilized for descriptive and statistical analyses.
AUTOMATICITY EXPECTATIONS 149
Table 3.3
Research Questions in the Survey Instrument
Research Questions Survey Questions Demographic Information on Music Teacher Educator 2-7 participants
Demographic Information on the Music Teacher 8-9, 23 Education Program
1. How were music teacher education programs preparing 10-17, 21 the music educator skill set necessary for beginning instrumental music teachers?
2. How were music teacher education programs evaluating 18-20, 22 the music educator skill set necessary for beginning instrumental music teachers?
3. What teaching skills did music teacher education 25, 27, 29, programs expect to be automatic in their graduating 31, 35, 36 instrumental music teachers, as reported by music teacher educators?
4. What performance skills did music teacher education 24, 26, 28, programs expect to be automatic in their graduating 30, 32-34, instrumental music teachers, as reported by music teacher 37 educators?
a. Analysis for possible correlations between 8-23, 24-37 program attributes and expectations (see Appendix F)
b. Analysis for possible correlations between music teacher educator experience and 2-3, 24-37 expectations
c. Analysis for possible patterns of expectations 24-37 according to learning domains, Bloom’s revised taxonomy, types of teacher knowledge, the InTASC standards, and the NASM competencies (see Appendices A and G)
AUTOMATICITY EXPECTATIONS 150
Validity and Reliability
Validity and reliability of the survey were established in several ways.
Validity refers to whether an instrument measures that which it has been designed to measure. Content validity, or how representative the measurement tool was to the domain being measured, was established by: (a) grounding the survey in related literature, (b) conducting a pilot study, (c) obtaining feedback from participants in the pilot study, and (c) consulting with and attaining approval from a faculty committee
(Leedy & Ormrod, 2010). Achieving content validity helped ensure the survey epitomized undergraduate music educator preparation and music educator skills.
Reliability, or whether results of the measurement instruments were consistently achieved, was established by ensuring wording was clear and concise throughout the survey instrument (Leedy & Ormrod, 2010). Explanations of what was meant by automaticity, teaching skills, and performance skills appeared in the survey in order to clarify the intent of the researcher.
Interrater reliability was also obtained for the Music Educator Skills and
Categorizations table (see Appendix A). Three music teacher educators associated with the researcher’s institution, but not on the dissertation committee, replicated the researcher’s six varieties of categorizations in this table. In its entirety, the table had a moderately strong level of reliability (r = 0.63), although the coefficients of individual categorizations varied somewhat:
• Types of music educator skills (r = 0.90) (Very strong reliability);
• Types of teacher knowledge (r = 0.78) (Strong reliability);
• Learning domains (r = 0.70) (Strong reliability); AUTOMATICITY EXPECTATIONS 151
• InTASC standards (r = 0.53) (Moderate reliability);
• Bloom’s revised taxonomy (r = 0.42) (Moderate reliability); and
• NASM competencies (r = 0.35) (Weak reliability).
The lower levels of interrater reliability in the standards, taxonomy, and competencies may be representative of how music teacher educators and institutions often interpret these categorizations differently. One institution might believe certain activities or experiences meet some levels or fulfill specific requirements, but this may not be identical for another institution. While one of the reasons behind these categorizations may be standardization across institutions, they still are left to the interpretation of individual practitioners and programs. Additionally, interpretation may be effected by the complexity of these categories.
AUTOMATICITY EXPECTATIONS 152
CHAPTER 4
Results
The purpose of this study was to examine instrumental music teacher educators’ perceptions regarding the level of automaticity of teaching knowledge and skill expected of beginning instrumental music teachers. The researcher explored how music teacher education programs were preparing and evaluating the necessary skill set for beginning instrumental music teachers, in addition to investigating what expectations music teacher educators had for teaching and performance skills in beginning instrumental music teachers.
Music Teacher Education Preparation
Participants in this study (n = 303) identified attributes of the music teacher education programs in which they taught. Most of the institutions represented were public (n = 198) or private (n = 98). An additional four respondents indicated they taught at a conservatory, while two represented community colleges. Two music teacher educators were currently adjunct instructors at both public and private institutions.
Coursework. Music teacher educators were asked whether their programs utilized semesters, trimesters, or quarters in order to compare the frequency of coursework with potentially different types of scheduling. The majority of institutions appeared to use semesters (n = 288). Eleven participants indicated scheduling was on quarters, while three used trimesters. One music teacher educator did not respond to this question. Based on the prevalence of semesters in responses, the researcher assumed the same variety for this institution to allow for statistical analysis and AUTOMATICITY EXPECTATIONS 153 comparison. In computation of the frequency of courses and experiences across programs, semesters, trimesters, and quarters were considered 1, 0.67, and 0.5 respectively.
Participants indicated how many courses in music theory, music history, primary instrument lessons, large ensembles (e.g., band, orchestra, choir), small ensembles (e.g., jazz bands, chamber orchestras, quartets) and conducting were required in undergraduate instrumental music education programs. Table 4.1 indicates the average requirement across programs, as well as the most frequent responses. The results reported in this table omitted participants who marked “Unknown” or skipped the question: (a) Music Theory (n = 15), (b) Music History (n = 16), (c) Applied
Lessons (n = 17), (d) Large Ensembles (n = 9), (e) Small Ensembles (n = 56), and
(f) Conducting (n = 10). These might be indicative of faculty who taught a few music education classes, but had limited knowledge or involvement in the total music teacher education curriculum.
Table 4.1
Course Requirements for Instrumental Music Education Programs
Course M SD Mode Applied Lessons 6.95 1.29 7 Large Ensembles 6.89 1.40 7 Music Theory 4.47 1.20 4 Music History 3.11 1.03 3 Conducting 2.03 0.81 2 Small Ensembles 1.90 2.38 0 Note. Those who indicated “Unknown” or skipped the question were omitted from this table.
One hundred and fifty-nine music teacher educators (52%) conveyed that music technology was required for undergraduate music education students at their institution, which represented 52.5% of the responses. An additional 79 participants AUTOMATICITY EXPECTATIONS 154
(26%) indicated, while this type of course was not a required part of the music education curriculum, it was offered as an elective. Fifty-eight music teacher educators (19%) communicated that music technology was neither required nor offered.
Music teacher education programs can often be quite dissimilar in the technique classes they require for instrumental students. Survey responses confirmed most of the technique classes required for all instrumental music education students involved those instruments typically found in band and orchestra ensembles. Voice techniques and piano were also required by a majority of programs (see Table 4.2).
Table 4.2
Technique Class Requirements for Instrumental Music Education Programs
Not required Required Class Not offered Offered Some* All Brass 4 6 34 254 Woodwind 4 6 33 255 Percussion 9 1 30 259 String 6 6 36 249 Voice 13 27 47 201 Guitar 76 101 27 59 Harp 224 30 1 3 Recorder 165 21 15 58 Piano 5 11 9 272 * This column indicates technique classes required for some instrumental students depending on their specialization (band or orchestra). Note. Those who indicated “Unknown” or skipped the question were omitted from this table.
Music teacher educators were also asked if and how their institutions incorporated laboratory ensembles into the preparation of instrumental music teachers. This type of experience could provide additional opportunities to practice secondary instruments and conducting skills. Results suggest orchestral lab ensembles were less likely to be required or offered at institutions. Furthermore, laboratory AUTOMATICITY EXPECTATIONS 155 bands were required for all instrumental music education students more often than orchestras. Institutions that required laboratory ensembles for some or all of the instrumental music education students were more likely to require and offer these experiences for conducting purposes, rather than playing secondary instruments (see
Table 4.3). Forty-six music teacher educators (15%) reported that all instrumental music education majors at their institution were required to play secondary instruments in both laboratory band and orchestra. Slightly more (n = 55, 18%) indicated all instrumental music education majors were required to conduct in both laboratory ensembles. Lastly, 40 participants (13%) reported that all instrumental music education students were required to play secondary instruments and conduct in both laboratory band and orchestra. Overall findings suggest music education programs were more likely to offer and require laboratory band than laboratory orchestra.
Table 4.3
Laboratory Ensemble Requirements
Not required Required
Ensemble Not offered Offered Some* All Secondary Instrument Performance
Band 90 75 21 86 Orchestra 166 31 14 64 Total 256 106 35 150 Conducting
Band 73 31 22 145 Orchestra 136 20 30 91 Total 209 51 52 236 * This column indicates technique classes required for some instrumental students depending on their specialization (band or orchestra). AUTOMATICITY EXPECTATIONS 156
Field experiences. Field experiences can provide pre-service teachers with the opportunity to practice teaching skills and develop a realistic perspective of the profession during their preparation. The number of courses with a field experience component involving some type of teaching varied widely between programs. As can be seen in Figure 4.1, the largest number of music teacher educators reported two, three, and four classes with these specific types of experiences.
Figure 4.1
Frequency of Courses with Field Experience Teaching Components
Participants were asked to estimate how many hours of teaching in field experiences were required of instrumental music education students prior to student teaching. Of the music teacher educators who gave a specific answer, there appeared to be a wide range of responses, varying from zero to six hundred hours. (However, six hundred hours appeared to be an outlying figure.) The average number of hours was 89.61 (SD = 79.34). With the outlier removed, the new average was slightly less
(M = 85.5, SD = 72.07). For both calculations, the median was 75 indicating responses were positively skewed. One hundred hours was the most frequent response AUTOMATICITY EXPECTATIONS 157
(n = 32), but with such high standard deviations, responses differed widely and little agreement was evident. Nine individuals indicated no hours were required and one responded with a single hour. Twelve music teacher educators specified that a number of weekly hours were required for some semesters. Four individuals responded that the number of hours varied, depending on specific class requirements and the placement with certain cooperating teachers.
The researcher supplied a list of other potential practice opportunities for pre- service music teachers and asked music teacher educators to respond on a Likert-type scale concerning the existence, undergraduate involvement, and requirement for these in their programs. The list included (a) peer teaching through courses; (b) tutoring or teaching private lessons; (c) assisting in local schools (e.g., marching band, sectionals, etc.); (d) youth activities affiliated with the institution (e.g., ensembles,
String Projects, preparatory divisions); (e) music camps connected with the institution; and (f) service learning in local schools. These experiences appeared to be most frequently not required, but undergraduates did have some involvement (see
Table 4.4). Peer teaching was reported as the most required with much student involvement. University-affiliated music camps appeared to be least required.
AUTOMATICITY EXPECTATIONS 158
Table 4.4
Undergraduate Involvement in Other Types of Field Experiences
Some involvement Much involvement Not Not Not Experience offered Required Required Required Required Peer teaching 14 42 93 11 132 Tutoring/Lessons 41 154 36 36 21 Local schools 19 150 37 57 33 Youth activities 71 150 25 36 11 Music camps 103 135 10 38 3 Service learning 57 137 35 27 30 Total 305 768 236 205 230 % 16.78 42.24 12.98 11.28 12.65 Note. Those who indicated “Unknown” or skipped the question were omitted from this table.
Music teacher educators also had the opportunity to describe other varieties or provide further information on field experiences offered in their programs. One university collaborated with a local church, enabling it to provide experiences similar to laboratory ensembles, in addition to the opportunity to teach private lessons. At another institution, undergraduates taught and conducted within the context of a New
Horizons program while enrolled in a methods class. Service learning was a recent addition to one music teacher educator’s program, but pre-service teachers volunteered rather than participated as a requirement. A different college provided a seven week chamber music experience in which pre-service teachers worked with middle school students. An additional participant reported that tutoring sometimes occurred before student teaching, but varied and was often dependant on cooperating teachers.
Student teaching and certification. The majority of music teacher education programs (87%) culminated in a one-semester student teaching period (n = 264). Pre- service teachers had two semesters, or a full academic year, of student teaching AUTOMATICITY EXPECTATIONS 159 according to 14 music teacher educators (5%). An additional ten reported student teaching lasted less than a semester, while nine reported student teaching went beyond two semesters. Three participants commented that student teaching was full time for one semester, but also involved students working part-time in the school during the preceding semester. Student teaching was part of a five-year graduate program that was a direct extension of the undergraduate degree at five of the music teacher educators’ institutions. The overall average length of student teaching was slightly over one semester (M = 1.10,
SD = 0.41).
Upon graduation, most instrumental music educators (68%) who worked with this survey’s population earned a PK-12 Music teaching certificate or license
(n = 206), which qualified them to teach all musical subjects. More specific to band and orchestra instruction, 68 music teacher educators (22%) reported pre-service teachers would receive a PK-12 Instrumental Music certification. Fewer programs prepared students for K-12 Music (n = 5) or K-12 Instrumental/General Music (n = 3) certification. Some students of participants worked toward certificates particular to secondary instruction: 6-12 Music (n = 4) and 6-12 Instrumental Music (n = 4). Four music teacher educators indicated pre-service teachers decided what certificates they received depending on coursework in which they enrolled (e.g., 6-12 Secondary certificate with optional dual certification for K-12; Secondary 7-12 Music or K-12
Music; Elementary or Secondary Education; and K-12 Instrumental, K-12 Vocal, or
K-12 Instrumental-Vocal). Other diverse certifications were also mentioned:
• Birth-21 or Birth-21 Instrumental; AUTOMATICITY EXPECTATIONS 160
• K-12 Music Broad Field;
• PK-12 Instrumental, Choral, and General; and
• PK-12 Instrumental and Vocal.
Music Teacher Education Evaluation
The preceding section discussed how instrumental music educators were being prepared in programs represented by this study’s population of teacher educators. This next section examines how pre-service teachers’ music educator skills are assessed throughout their preparation. The researcher provided a sample list of evaluative tools and asked participants to identify all that were used in their program
(see Figure 4.2). Most music teacher educators indicated the utilization of written and verbal feedback, self evaluation, and rubrics. Action research projects appeared to be least used in the represented programs. Figure 4.3 illustrates how many evaluative tools music teacher educators indicated they were using, ranging from 1 (n = 2) to all
12 (n = 8). The largest number of participants (n = 80, 26%) used seven of the sample evaluative tools.
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Figure 4.2
Frequency of Evaluative Tools used in Music Teacher Education
Note. Those who indicated “Unknown” or skipped the question were omitted from this table.
AUTOMATICITY EXPECTATIONS 162
Figure 4.3
Frequency of Evaluative Tools used in Music Teacher Education
Participants also had the opportunity to specify what other evaluative tools were being used in their music teacher education programs. Incorporation of video technology was frequently mentioned. Some music teacher educators used video for instructor assessment and self-reflection, having pre-service teachers watch their own teaching at various points in the semester. Others mentioned both faculty and peer involvement in feedback and evaluation with video. Another specifically referred to the SCRIBE program for this purpose. Some music teacher educators referred to
Teacher Work Samples and Teacher Performance Assessments. (One described the latter as an action research project in which pre-service teachers planned, taught, and reflected on a specific lesson sequence.) Other evaluation tools mentioned by individual participants included: (a) a unit plan and evaluation during student teaching; (b) debate; (c) an assessment of method notebooks, in which pre-service teachers collected methods, articles, and instructional tools; (d) performance-based assessment by professors; and (e) a senior thesis. AUTOMATICITY EXPECTATIONS 163
Evaluation across programs. One hundred and six music teacher educators
(35%) used evaluative tools across multiple courses in the music teacher education program. These participants were asked to describe how their institutions accomplished this. The researcher coded these responses, resulting in emerging themes that focused on: (a) who did the evaluation, (b) how the evaluation occurred
(i.e., tools), (c) what was the focus of the evaluation, (d) when did evaluations take place, and (e) how these evaluations were used across courses.
Fourteen participants specifically mentioned who conducted the evaluation across courses. Five used self-evaluation, while four involved a group of music or music education faculty, three specified individual instructors. Additional single responses included evaluations conducted by student observers and supervisors for field experience, practicum, and student teaching. Music teacher educators used a variety of evaluative tools, but the most prevalent were rubrics (n = 22) and ePortfolios (n = 20). There were also 17 different tools mentioned by individual participants. All tools used to assess across pre-service music educator skills in the represented music teacher education programs are organized in Table 4.5.
AUTOMATICITY EXPECTATIONS 164
Table 4.5
Evaluative Tools used across Music Teacher Education Programs
Evaluative tools n Specific examples given by participants Rubric 22 ePortfolio 20 Portfolio 8 Video 8 Exams/Tests 6 Conducting Exam, Music Education Fundamentals Exam, Music History Exam, Music Major Field Exit Test, National Teacher Exam, Written and performing final exams Teacher Work Sample 6 Peer Teaching 4 Reflection 4 Guided reflection, Reflection on how artifacts meet state competencies, Reflections on field experiences, Reflective essay Rating Scales 3 University-standardized 3 Evaluation forms, Music version of a assessments university-mandated form, Student learning outcomes Checklists 2 Juries/Jury Forms 2 Lesson Plan Templates 2 Piano Skills Assessments 2 State-mandated assessments 2 Assessment form, Rubric Other 17 Artifacts, Course Notebook, Electronically submitted forms, Feedback forms, Handbook for Beginning Music Teachers, Mini-Teaching Lessons, Music Repertoire Project, Observation reports, Vocal Techniques Assessment, Instrumental Techniques Assessment, Plan-Teach-Reflect Model, Qualitative Research Journal, Site evaluation forms, Student teacher discussion/seminars, Unit Plan Assessment, Written Feedback
These evaluations focused on a wide variety of skills and characteristics.
Some could be described as general teaching skills (n = 26), including lesson planning, communication (i.e., lesson delivery, public speaking, rapport), and AUTOMATICITY EXPECTATIONS 165 evaluation (i.e., error detection, general evaluation skills, and monitoring/feedback).
Other subcategories mentioned by individuals included (a) awareness of students, (b) classroom performance, (c) flexibility, (d) management, (e) music education fundamentals, (f) professional attributes, (g) domains involved with teaching in and out of the classroom, (h) standards for professional practice, and (i) universal teaching knowledge.
Sixteen music teacher educators identified the overarching category of dispositions as one of the concentrations of evaluations used across their programs.
One respondent defined dispositions as “non-instructional characteristics necessary for projected success in the teaching profession.” Provided examples of desired dispositions included reliability, empathy, reception to feedback, and initiative.
Some systems of evaluation concentrated on more specific skills, such as those associated with certain instruments. Examples that were mentioned included:
(a) instrumental fingerings, techniques, and/or transpositions, (b) percussion rudiments, (c) performance skills, (d) piano proficiency, skills, and/or technique, (e) secondary instrument performing skills, and (f) vocal techniques. Ten music teacher educators made some comment that fit into this category. An additional 6 stated some of their across program evaluations focused on conducting skills.
Six participants indicated organizations, beyond their institution, established competencies upon which their programs’ evaluations were based. Those specified were the InTASC standards, the NCATE competencies, and standards associated with certain states. (Many evaluation methods in other represented programs of this study are likely at least partially based on such outside mandates.) Ten additional comments AUTOMATICITY EXPECTATIONS 166 were either vague or did not clearly fit into one of the larger categories (i.e., content competency, field experience, interview, music history knowledge, music skills, proficiency testing, sight singing, skills in general, and tonal patterns).
When pre-service music teachers were evaluated differed much between programs. Some occurred during particular academic years (n = 11). The most frequently specified time for overall assessment in the program was during the sophomore year (n = 4). These individuals indicated performance skill testing, unit plan assessments as part of practicum and methods courses, checklists, and skills requirement occurred during this second year. One music teacher educator identified a self-evaluation survey completed by freshmen. The two examples of junior year evaluation were performance skill testing and a review by music education faculty.
According to three music teacher educators, senior year assessments included the development of ePortfolios and a graduations self-evaluation survey, as well as an additional faculty review.
Seven music teacher educators described evaluation occurring throughout pre- service teachers’ programs. More explicitly, one music teacher educator stated student work and efforts were evaluated at the end of each academic year. For another, students were reviewed in each course. An additional music teacher educator specified the ePortfolio was developed throughout the program. Finally, another participant indicated students often performed self-evaluations of their music educator skills.
Other music teacher educators (n = 7) explained evaluation occurred at specific times throughout the program. For example, one disclosed that some AUTOMATICITY EXPECTATIONS 167 evaluation happened at the middle and end of the term within field experiences in methods courses. Others reported that a sequence of assessments transpired over three to four semesters of certain courses, such as one institution’s use of a Teacher Work
Sample evaluation. Two participants gave vague statements about when certain evaluations took place (i.e., before practicum, before student teaching). Additionally, two noted that an evaluation, in these cases a Teacher Work Sample or disposition assessment, occurred at three distinct points in the program. However, neither music teacher educator specified the exact timing of these.
The researcher also examined how evaluations were used across courses, mainly looking for responses indicating certain courses or experiences used the same types of evaluation. Thirteen music teacher educators described how some evaluations were shared between music education coursework, pre-student teaching field experiences and student teaching itself. Identical evaluations between different courses were identified by ten participants. For example, one institution used the same assessments in middle and high school methods classes. Another shared tools between several methods and technique courses. Seven music teacher educators indicated some type of evaluation was shared throughout all music education courses, either using specific tools such as the ePortfolio, or all supporting certain program requirements.
University supervision during student teaching. Earlier in this chapter, findings concerning the length of the student teaching period in music teacher education were identified. Most music teacher educators (n = 264) revealed that pre- service teachers completed their preparation with a one-semester period of student AUTOMATICITY EXPECTATIONS 168 teaching. However, the frequency of university supervisor observations was not as straightforward. Student teachers were observed three to four times at the institutions of 166 music teacher educators (55%). One hundred and one participants (33%) reported more than four observations during this period. Observations were limited to one or two times in 19 of the represented programs (6%). Lastly, three reported that no university supervisor observed the student teacher. This was in direct contrast to other music teacher educators who reported extensive observation. Two participants observed student teachers weekly, one of which also stated that this was more than twice as much as other local institutions. Two other music teacher educators indicated student teachers were observed each time they taught. (However, this might have been mistaken for another practicum experience, rather than an entire semester of student teaching.) A different music teacher educator commented that his answer would depend on what was meant by “practicum.” In this case, the participant clarified that faculty accompanied all students at that institution’s practicum, but observed four times during the full-time internship. Additional confusion in meaning might have affected other results.
Automaticity Expectations of Music Educator Skills
The preceding results provided relevant information about how different music teacher education programs were preparing and evaluating pre-service instrumental music teachers in order to develop the skill set necessary for beginning teachers. The following section reports expectations of automaticity for beginning instrumental music teachers educated at participants’ institutions. As described in the forward, one could consider two types of essential music educator skills: teaching and AUTOMATICITY EXPECTATIONS 169 performance. The term “teaching skill” refers to instructional abilities representative of pedagogical and pedagogical content understanding. “Performance skills” signify content knowledge that can be physically demonstrated. The automaticity expectations communicated in survey responses will be initially discussed within this dichotomy. The chapter will then conclude with further analysis of the expectations as a whole, involving different skill categorizations and potential correlations with findings presented earlier in the study.
Teaching skills. The organization of survey questions in the automaticity expectations section was primarily based on the categories of teaching and performance skills. Those larger topics considered in the teaching variety included:
(a) teaching skills related to theoretical knowledge, (b) teaching skills related to historical knowledge, (c) primary instrument teaching skills, (d) teaching skills on secondary instruments, (e) classroom management skills, (f) general rehearsal skills, and (g) technological skills. However, the distinction between teaching and performing is not as simple. The researcher determined two teaching skills could also be considered performance skills (i.e., model basic to advanced performance technique/music on one’s primary instrument and model basic performance technique/music on secondary instruments). Furthermore, the researcher viewed the ability to demonstrate classification of historical styles from aural examples as more of a performance skill than teaching even though it was included in the teaching skills related to historical knowledge survey question.
Participants ranked their automaticity expectations for each skill on a four- point Likert-type scale. The relevance of the following results in terms of AUTOMATICITY EXPECTATIONS 170 automaticity is shown in Table 4.6. The category of teaching skills that received the highest automaticity expectations at the approaching automatic level was teaching skills related to one’s primary instrument (M = 3.07, SD = 1.04). This was followed by classroom management skills (M = 2.80, SD = 0.95), general rehearsal skills
(M = 2.80, SD = 0.95), teaching skills on secondary instruments (M = 2.78,
SD = 0.91) and technological skills (M = 2.78, SD = 1.34). The lowest rated teaching skills were those related to theoretical knowledge (M = 2.77, SD = 0.97) and historical knowledge (M = 2.34, SD = 1.08).
Table 4.6
Automaticity Expectations Scale
Range Level Description 1.00-1.99 Nonautomatic The beginning teacher MUST completely focus on this skill while performing it.
2.00-2.99 Beginning automatic The beginning teacher can perform the skill while OCCASIONALLY focusing on something else.
3.00-3.99 Approaching automatic The beginning teacher can perform the skill while MOSTLY paying attention to something else.
4.00 Automatic The beginning teacher can perform the skill while TOTALLY paying attention to something else.
Table 4.7 displays the ranking of automaticity expectations for individual teaching skills. Most were expected to be at the beginning automatic level, with the exception of the top ten at the approaching automatic level. Three of the top ten skills dealt with using technology, which could be incorporated into teaching. Despite the finding that several technological skills ranked high in expectations, four also AUTOMATICITY EXPECTATIONS 171 appeared at the bottom of the list. Four of the top ten teaching skills involved primary instruments. The least expected teaching skill, using website construction software, was the only skill overall to be at the nonautomatic level. No skills were expected to be completely automatic.
Table 4.7
Ranking of Automaticity Expectations for Teaching Skills
Rank Category Skill M SD 1 Technology Use communication technology (e.g., Email, Blogs) 3.71 0.80 2 Technology Use word processing software (e.g., Microsoft Word) 3.68 0.78 3 Primary Inst. Model basic to advanced performance technique/music 3.39 0.87 4 Technology Use iTunes 3.26 1.31 5 Primary Inst. Explain basic to advanced performance technique 3.16 0.93 6 Rehearsal Follow an established classroom routine 3.09 0.91 7 Primary Inst. Assess student technical errors quickly and accurately 3.05 0.91 8 Management Use eye contact effectively 3.04 0.88 9 Primary Inst. Sequence instruction in order to set up a beginner on 3.00 1.04 one’s primary instrument 10 Technology Use notation software (e.g., Finale, Sibelius) 3.00 1.06 11 Primary Inst. Assess student intonation errors quickly and accurately 2.96 0.92 12 Rehearsal Communicate the rehearsal plan and goals to students 2.96 0.95 13 Management Give clear instructions 2.90 0.89 14 Theory Assess student rhythmic errors quickly and accurately 2.89 0.87 15 Secondary Inst. Explain basic performance technique 2.89 0.88 16 Rehearsal Devise and carry out a warm up routine based on 2.89 0.96 specific technical or musical goals 17 Management Detect positive and negative student behavior 2.87 0.95 18 Technology Use database software (e.g., Excel) 2.86 1.30 19 Technology Use presentation software (e.g., PowerPoint, KeyNote) 2.86 1.25 20 Theory Sequence instruction in order to introduce rhythmic 2.85 1.00 counting to beginners 21 Primary Inst. Correct student errors by modifying instruction and 2.85 1.03 reinforcing corrections 22 Management Give meaningful, verbal feedback 2.82 0.91 23 Secondary Inst. Model basic performance technique/music 2.81 0.86 24 Technology Use AV equipment (e.g., DVD/CD Players, Camcorder, 2.81 1.26 Digital Camera) 25 Theory Sequence instruction in order to introduce music 2.80 0.99 notation to beginners 26 Secondary Inst. Assess student intonation errors quickly and accurately 2.79 0.87 27 Technology Use office equipment (e.g., Copier, Scanner, Fax 2.79 1.4 Machine)
AUTOMATICITY EXPECTATIONS 172
28 Theory Correct student rhythmic errors by modifying instruction 2.78 0.89 and reinforcing corrections 29 Secondary Inst. Sequence instruction in order to set up a beginning 2.77 1.01 instrumentalist 30 Secondary Inst. Assess student technical errors quickly and accurately 2.76 0.88 31 Rehearsal Adjust lesson plans to meet the needs of the learning 2.71 0.93 context 32 Management Use proximity as a behavior modification 2.70 1.02 33 Rehearsal Include closure at the end of the rehearsal with feedback 2.69 0.95 and plans for future practice 34 Secondary Inst. Correct student errors by modifying instruction and 2.67 0.93 reinforcing corrections 35 Management Use extrinsic motivators 2.67 1.01 36 Rehearsal Adjust pacing to meet the needs of the learning context 2.66 0.91 37 Management Promote intrinsic motivation 2.62 0.93 38 Rehearsal Ask questions that promote critical thinking or higher- 2.57 0.94 order knowledge 39 History Design instruction to connection music with history 2.37 1.08 40 Theory Assess transposition errors quickly and accurately 2.35 0.95 41 History Sequence instruction in order to teach historical and 2.32 1.08 musical aspects of composers 42 Theory Correct transposition errors by giving instruction and 2.31 0.97 reinforcing corrections 43 History Sequence skills for student comprehension of historical 2.27 1.08 forms 44 History Sequence instruction in order to teach historical styles of 2.26 1.09 performance practices 45 Technology Use practicing software (e.g., Smart Music) 2.22 1.35 46 Technology Use sound equipment (e.g., Amps, Microphones, 2.20 1.31 Recording Equipment) 47 Technology Use GarageBand 2.13 1.51 48 Technology Use website construction software (e.g., Sea Monkey, 1.81 1.40 Adobe Dreamweaver)
Some cross-categorical teaching skills emerged in initial preparation for analysis (see Appendix G). The following skills were represented in more than one category: (a) modeling (M = 3.1, SD = 0.41), (b) communication (M = 2.90,
SD = 0.54), (c) sequencing of skills for beginners (M = 2.86, SD = 0.10), (d) error detection (M = 2.80, SD = 0.25), (e) adaptive expertise (M = 2.76, SD = 0.29), and (f) error correction (M = 2.65, SD = 0.24). No significant difference was found among automaticity expectations for these groups of skills (F(5,28) = 0.540, p < 0.5). AUTOMATICITY EXPECTATIONS 173
Performance skills. Larger topics in the performance category of music educator skills included abilities to perform on: (a) primary instruments,
(b) secondary instruments, (c) voice, and (d) piano. Additionally, other topics were considered in this category: (a) demonstrated theoretical knowledge, (b) demonstrated historical knowledge, and (c) conducting abilities. These rankings appeared to be more spread out than their teaching counterparts. Performance skills related to primary instruments received the highest automaticity expectation ranking (M = 3.52,
SD = 0.86), similar to teaching primary instruments. These were followed by conducting skills (M = 3.25, SD = 0.95) and performing on secondary instruments
(M = 3.01, SD = 0.96). The remaining skills received lower rankings:
(a) demonstration of historical knowledge (M = 2.68, SD = 1.07), (b) voice performance skills (M = 2.64, SD = 1.32), demonstration of theoretical knowledge
(M = 2.61, SD = 1.14), and piano performance skills (M = 2.45, SD = 1.24). Table 4.8 organizes all performance skills used in this study. Primary instrument skills represented seven of the top ten ranked automaticity expectations in the performance category. At the lower end of rankings, there appeared to be several theory and piano skills. Performance skills generally received higher expectations than teaching skills, although none reached the automatic level. Twenty-seven performance skills were expected to be approaching automatic. The remaining 34 were at the beginning automatic level.
AUTOMATICITY EXPECTATIONS 174
Table 4.8
Ranking of Automaticity Expectations for Performance Skills
Rank Category Skill M SD 1 Primary Inst. Hold Instrument Properly 3.82 0.62 2 Primary Inst. Demonstrate good posture 3.82 0.63 3 Primary Inst. Exhibit appropriate breath support (when applicable) 3.76 0.66 4 Primary Inst. Demonstrate professional sound production technique 3.70 0.71 5 Primary Inst. Demonstrate proper fingering or performance techniques 3.69 0.76 for written notation (when applicable) 6 Primary Inst. Tune instrument/Match pitch 3.63 0.73 7 Conducting Perform basic conducting patterns (in 1,2,3, 4) clearly in 3.60 0.78 right hand only 8 Conducting Hold baton properly 3.58 0.75 9 Primary Inst. Produce a professional-level of tone quality 3.57 0.77 10 Secondary Inst. Demonstrate good posture 3.57 0.79 11 Theory Interpret basic musical terminology while performing on 3.54 0.85 one’s primary instrument 12 Primary Inst. Perform articulation technique at a professional-level 3.53 0.78 (bowing, tonguing) 13 Conducting Maintain steady beat while conducting 3.51 0.79 14 Secondary Inst. Hold instruments properly 3.47 0.83 15 Conducting Perform basic conducting patterns (in 1,2,3, and 4) 3.43 1.04 clearly in both hands (mirroring) 16 Primary Inst. Perform with a professional-level of intonation 3.42 0.81 17 Primary Inst. Model basic to advanced performance technique/music 3.39 0.87 18 Primary Inst. Justify alternate fingering options (when applicable) 3.37 0.93 19 Conducting Cut off the ensemble 3.37 0.90 20 Secondary Inst. Use basic breath support (when applicable) 3.36 0.83 21 Secondary Inst. Assemble instruments (when applicable) 3.28 0.88 22 Conducting Perform tempo changes while conducting 3.15 0.91 23 Conducting Perform basic subdivisions clearly in right hand only 3.11 0.97 24 Conducting Perform dynamic changes while conducting 3.05 0.93 25 Secondary Inst. Tune instruments 3.03 0.90 26 Conducting Hold and release fermatas 3.02 0.96 27 Piano Demonstrate good posture 3.01 1.29 28 Voice Demonstrate good posture 2.98 1.35 29 Secondary Inst. Perform basic articulations (staccato, legato, slurs, 2.97 0.85 accents) 30 Conducting Perform basic cueing gestures 2.96 0.95 31 Secondary Inst. Demonstrate basic sound production technique 2.95 0.89 32 Conducting Perform basic subdivisions in both hands 2.91 1.09 33 Secondary Inst. Produce a good tone quality 2.87 0.87 34 Secondary Inst. Demonstrate proper fingering or performance technique 2.86 0.86 for basic written notation 35 Secondary Inst. Model basic performance technique/music 2.81 0.86 AUTOMATICITY EXPECTATIONS 175
36 Voice Demonstrate ability to match pitch 2.81 1.31 37 Theory Identify aspects of harmony visually (e.g., intervals, 2.76 1.03 inversions, chord qualities, cadences) 38 History Execute appropriate historical styles or performance 2.76 1.10 practices on one’s primary instrument 39 Theory Interpret the concert pitch for transposed parts 2.75 1.01 40 Theory Identify musical forms (e.g., melodic phrases, single 2.73 1.03 movements, multi-movement works) 41 Secondary Inst. Demonstrate good intonation 2.73 0.89 42 History Identify historical and musical aspects of composers 2.70 1.07 from different musical eras 43 History Classify different historical styles from aural examples 2.68 1.04 44 Voice Demonstrate good intonation 2.68 1.28 45 History Identify historical forms characteristic of different 2.60 1.07 musical eras 46 Piano Perform basic articulations (staccato, legato, accents) 2.57 1.26 47 Theory Notate dictated rhythmic aural examples 2.55 1.12 48 Theory Identify aspects of harmony aurally (e.g., intervals, 2.51 1.07 inversions, chord qualities, cadences) 49 Theory Perform a melody by ear on one’s primary instrument 2.49 1.16 50 Voice Produce a good tone quality 2.49 1.26 51 Piano Perform basic chord progressions (I-IV-V7-I) in most 2.49 1.20 major keys 52 History Demonstrate classification of historical styles from aural 2.48 1.08 examples 53 Primary Inst. Perform basic repairs 2.46 1.09 54 Theory Notate dictated melodic aural examples 2.43 1.13 55 Theory Perform notated music in different clefs on one’s 2.38 1.21 primary instrument 56 Theory Perform transposed music on one’s primary instrument 2.26 1.05 57 Voice Perform with good diction 2.26 1.29 58 Piano Use pedal while performing 2.25 1.26 59 Piano Perform simple accompaniments 2.22 1.12 60 Piano Play melody in right hand and harmonized 2.14 1.12 accompaniment in left hand 61 Secondary Inst. Perform basic repairs 2.08 1.02
The overall mean for automaticity expectations for teaching skills was 2.77
(SD = 0.37) and 2.97 (SD = 0.48) for performance skills. A t-test for independent samples showed a statistically significant difference between these two groups,
(t107 = -2.44, p < 0.5). Table 4.9 compares the overall averages of the larger topics in both performance and teaching skills. A one-way ANOVA was used to assess the AUTOMATICITY EXPECTATIONS 176 expectation differences among the fourteen categories of skills. Automaticity expectations were significantly different across the categories (F(13,93) = 7.06, p < 0.5).
Table 4.9
Comparison of Expectations of Performance and Teaching Skill Categories
Skill Category M SD Performance skills on primary instruments 3.52 0.86 Conducting skills 3.25 0.95 Teaching skills on primary instruments 3.07 1.04 Performance skills on secondary instruments 3.01 0.96 Classroom management skills 2.80 0.95 General rehearsal skills 2.80 0.95 Teaching skills on secondary instruments 2.78 0.91 Technological skills 2.78 1.37 Demonstrated historical knowledge 2.68 1.07 Teaching skills related to theoretical knowledge 2.66 0.97 Performance skills on voice 2.64 1.32 Demonstrated theoretical knowledge 2.61 1.14 Performance skills on piano 2.45 1.24 Teaching skills related to historical knowledge 2.34 1.08
A Bonferroni post hoc analysis indicated significant differences between several categories. Performance skills on primary instruments showed the most difference, appearing significant compared to all other categories except teaching primary instruments, playing secondary instruments, and conducting skills. The only category that was not significantly different from any other categories was teaching skills on primary instruments. Table 4.10 organizes the significant differences between skill categories.
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Table 4.10
Significant Differences in Post Hoc Analyses of Automaticity Expectations
Category M Other Categories Significance
Performance skills on primary instruments 3.52 Demonstrated theoretical 0.00 knowledge
Teaching skills related to 0.00 theoretical knowledge
Demonstrated historical 0.01 knowledge
Teaching skills related to 0.00 historical knowledge
Teaching skills on secondary 0.00 instruments
Performance skills on voice 0.00
Performance skills on piano 0.00
Classroom management skills 0.00
General rehearsal skills 0.00
Technological skills 0.00
Conducting skills 3.25 Demonstrated theoretical 0.01 knowledge
Teaching skills related to 0.00 historical knowledge
Performance skills on piano 0.00
Performance skills on secondary 3.01 Teaching skills related to 0.03 instruments historical knowledge
Classroom management skills 2.80 Performance skills on primary 0.00 instruments
General rehearsal skills 2.80 Performance skills on primary 0.00 instruments
Teaching skills on secondary instruments 2.78 Performance skills on primary 0.00 instruments
Technological skills 2.78 Performance skills on primary 0.00 instruments
AUTOMATICITY EXPECTATIONS 178
Demonstrated historical knowledge 2.68 Performance skills on primary 0.01 instruments
Teaching skills related to theoretical 2.66 Performance skills on primary 0.00 knowledge instruments
Performance skills on voice 2.64 Performance skills on primary 0.00 instruments
Demonstrated theoretical knowledge 2.61 Performance skills on primary 0.00 instruments
Conducting skills 0.01
Performance skills on piano 2.45 Performance skills on primary 0.00 instruments
Performance skills on piano 0.00
Teaching skills related to historical 2.34 Performance skills on primary 0.00 knowledge instruments
Performance skills on 0.03 secondary instruments
Conducting skills 0.00
Further analysis.
Other categorizations. Automaticity expectations of beginning music educators’ performance and teaching skills were further analyzed through other categorizations. How each skill corresponded to learning domains, Bloom’s revised taxonomy, types of teacher knowledge, the InTASC standards, and the NASM competencies was identified in Appendix A. These diverse categories were analyzed for additional, potential patterns beyond how skills were originally organized in the survey.
The researcher arranged music educator skills according to the cognitive, psychomotor, and affective domains. Some skills could be included in more than one category. For example, interpreting basic music terminology while performing on AUTOMATICITY EXPECTATIONS 179 one’s primary instrument involved the cognitive activity of interpretation and subsequent transfer of this information into a psychomotor execution of skills on an instrument. Each domain was relatively close in automaticity expectations means, with psychomotor skills being highest (M = 2.91, SD = 0.48), followed by affective
(M = 2.85, SD = 0.22) and cognitive (M = 2.81, SD = 0.42) types. There was no statistically significant difference between these three domains.
Each music educator skill was also categorized according to Bloom’s revised taxonomy. In the researcher’s organization, skills identified in each category subsumed the less advanced levels. Those within the analyzing level, involving the ability to focus on individual components within a larger context, were ranked highest in automaticity expectations (M = 3.04, SD = 0.42) (Anderson et al., 2001). The resulting averages for other taxonomy levels were: (a) applying (M = 2.95,
SD = 0.52), (b) evaluating (M = 2.78, SD = 0.28), (c) creating (M = 2.70, SD = 0.31), and (d) understanding (M = 2.66, SD = 0.1). Results of a one-way ANOVA indicated no statistically significant difference between these levels (F(4, 98) = 1.824, p < 0.5).
Skills were also sorted by type of teacher knowledge (Shulman, 1986, 1987).
Automaticity expectations were highest for those skills associated with content knowledge (M = 2.96, SD = 0.47). Pedagogical (M = 2.8, SD =0.47) and pedagogical content (M = 2.72, SD = 0.29) were slightly less expected. The obtained value was
0.06 higher than the critical value in a one-way ANOVA conducted on these groupings, suggesting significance (F(2, 104) = 3.06, p = 0.51). A post-hoc analysis showed the difference to be between content and pedagogical content knowledge. AUTOMATICITY EXPECTATIONS 180
One finding when considering the InTASC standards in respect to music educator skills was similar to what was indicated with types of teacher knowledge.
Skills related to the fourth standard (content knowledge) were ranked highest in automaticity expectations (M = 2.95, SD = 0.49). The means of the other standards were rather closely grouped, with the lowest average automaticity expectation tied to the innovative applications of content standard (M = 2.57, SD = 0). No significant differences were found between these standards (F(8, 109) = 0.845, p < 0.5).
The most complex categorization of skills was through the NASM competencies, 41 of which were considered in Appendix A. Those equal for highest average in this category could be viewed as similar to the initial analysis of automaticity expectations in Tables 4.7 and 4.8, which pointed to high expectations for primary instrument skills. Likewise, the two lowest competencies relating to music history were comparable to the findings reported in Table 4.9. The complete
NASM findings are presented in Table 4.11.
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Table 4.11
Ranking of Automaticity Expectations within NASM competencies
Rank Competency M SD 1 An overview understanding of the repertory in their major performance area 3.66 0.14 and the ability to perform from a cross-section of that repertory
2 Technical skills requisite for artistic self-expression in at least one major 3.66 0.14 performance area at a level appropriate for the particular music concentration
3 The ability to develop and defend musical judgments 3.37 0.00
4 Knowledge of and performance ability on wind, string, and percussion 3.25 0.37 instruments sufficient to teach beginning students effectively in groups
5 The prospective music teacher must be a competent conductor, able to create 3.24 0.26 accurate and musically expressive performances with various types of performing groups and in general classroom situations
6 Knowledge and skills sufficient to work as a leader and in collaboration on 3.10 0.30 matters of musical interpretation. Rehearsal and conducting skills are required as appropriate to the particular music concentration
7 In addition to the skills required for all musicians, functional performance 3.07 0.50 abilities in keyboard and the voice are essential. Functional performance abilities in instruments appropriate to the student’s teaching specialization are also essential
8 Knowledge and skills sufficient to teach beginning students on instruments 3.05 0.48 and/or in voice as appropriate to the chosen areas of specialization
9 The ability to think, speak, and write clearly and effectively, and to 2.92 0.50 communicate with precision, cogency, and rhetorical force
10 An understanding of and the ability to read and realize musical notation 2.79 0.42
11 Ability to teach music at various levels to different age groups and in a variety 2.79 0.17 of classroom and ensemble settings in ways that develop knowledge of how music works syntactically as a communication medium and developmentally as an agent of civilization. This set of abilities includes effective classroom and rehearsal management
12 The ability to assess aptitudes, experiential backgrounds, orientations of 2.75 0.20 individuals and groups of students, and the nature of subject matter, and to plan educational programs to meet assessed needs
13 An understanding of evaluative techniques and ability to apply them in 2.74 0.23 assessing both the musical progress of students and the objectives and procedures of the curriculum
14 The ability to accept, amend, or reject methods and materials based on 2.69 0.03 personal assessment of specific teaching situations
AUTOMATICITY EXPECTATIONS 182
15 Students must acquire basic knowledge of music history and repertories 2.68 1.07 through the present time, including study and experience of musical language and achievement in addition to that of the primary culture encompassing the area of specialization
16 Sufficient understanding of and capability with musical forms, processes, and 2.67 0.09 structures to use this knowledge and skill in compositional, performance, analytical, scholarly, and pedagogical applications according to the requisites of their specializations
17 An acquaintance with a wide selection of musical literature, the principal eras, 2.66 0.05 genres, and cultural sources
18 The ability to place music in historical, cultural, and stylistic contexts 2.66 0.05
19 The ability to lead performance-based instruction in a variety of settings 2.66 0.00
20 Knowledge of content, methodologies, philosophies, materials, technologies, 2.61 0.55 and curriculum development for the area(s) of specialization (composition, electronic and computer music, ethnic music, guitar, small ensembles, jazz, keyboard, orchestral music, music history and theory, music in combination with other disciplines, music technologies, and popular music; or combinations of one or more of these types of content with aspects of the general, vocal/choral, or instrumental specializations)
21 While synthesis is a lifetime process, by the end of undergraduate study 2.60 0.14 students must be able to work on musical problems by combining, as appropriate to the issue, their capabilities in performance; aural, verbal, and visual analysis; composition/improvisation; and history and repertory
22 An understanding of the common elements and organizational patterns of 2.57 0.14 music and their interaction, the ability to employ this understanding in aural, verbal, and visual analyses, and the ability to take aural dictation
23 Knowledge of content, methodologies, philosophies, materials, technologies, 2.51 0.42 and curriculum development for instrumental music
24 The ability to hear, identify, and work conceptually with the elements of music 2.47 0.19 such as rhythm, melody, harmony, structure, timbre, texture
25 Keyboard competency 2.45 0.32
26 Knowledge and skill in the selected area(s) of specialization (composition, 2.45 0.35 electronic and computer music, ethnic music, guitar, small ensembles, jazz, keyboard, orchestral music, music history and theory, music in combination with other disciplines, music technologies, and popular music; or combinations of one or more of these types of content with aspects of the general, vocal/choral, or instrumental specializations) sufficient to teach beginning and intermediate students effectively
27 The ability to use instruments, equipment, and technologies associated with the 2.39 0.41 area(s) of specialization (composition, electronic and computer music, ethnic music, guitar, small ensembles, jazz, keyboard, orchestral music, music history and theory, music in combination with other disciplines, music technologies, and popular music; or combinations of one or more of these types of content with aspects of the general, vocal/choral, or instrumental specializations) AUTOMATICITY EXPECTATIONS 183
28 The prospective music teacher should be able to apply analytical and historical 2.38 0.50 knowledge to curriculum development, lesson planning, and daily classroom and performance activities. Teachers should be prepared to relate their understanding of music with respect to styles, literature, multiple cultural sources, and historical development, both in general and as related to their area(s) of specialization
29 An ability to address culture and history from a variety of perspectives 2.37 0.00 Note. Some music educator skills were associated with more than one NASM competencies. See Appendix A for clarification.
A one-way ANOVA indicated a significant difference between the preceding
NASM competencies (F(28, 252) = 7.704, p < 0.5). In order to conduct a Bonferroni post hoc analysis, competencies that only related to a single music educator skill in this study were omitted (i.e., 3, 19, 29). Many significant differences were apparent, with the exception of the fourteenth, sixteenth, and twentieth ranked competencies in the preceding table. All others are organized in Table 4.12. The first, second, and fourth highest ranked competencies had the most significant differences with other competencies.
AUTOMATICITY EXPECTATIONS 184
Table 4.12
Significant Differences in Post Hoc Analyses of Automaticity Expectations within
NASM competency rankings
Rank Competency M Other Competency Ranks p
1 An overview understanding of the 3.66 7 0.01 repertory in their major performance 8 0.01 area and the ability to perform from a 9 0.00 cross-section of that repertory 10 0.00 11 0.00 12 0.00 13 0.00 15 0.00 17 0.02 18 0.02 21 0.00 22 0.00 23 0.04 24 0.00 25 0.00 26 0.00 27 0.00 28 0.00
2 Technical skills requisite for artistic 3.66 7 0.01 self-expression in at least one major 8 0.01 performance area at a level 9 0.00 appropriate for the particular music 10 0.00 concentration 11 0.00 12 0.00 13 0.00 15 0.00 17 0.02 18 0.02 21 0.00 22 0.00 23 0.04 24 0.00 25 0.00 26 0.00 27 0.00 28 0.00
AUTOMATICITY EXPECTATIONS 185
4 Knowledge of and performance ability 3.25 10 0.05 on wind, string, and percussion 12 0.01 instruments sufficient to teach 13 0.04 beginning students effectively in 15 0.00 groups 21 0.02 22 0.02 24 0.00 25 0.00 26 0.00 27 0.01 28 0.00
5 The prospective music teacher must 3.24 15 0.01 be a competent conductor, able to 24 0.00 create accurate and musically 25 0.01 expressive performances with various 26 0.00 types of performing groups and in 27 0.03 general classroom situations 28 0.01
6 Knowledge and skills sufficient to 3.10 15 0.05 work as a leader and in collaboration 24 0.02 on matters of musical interpretation. 26 0.01 Rehearsal and conducting skills are required as appropriate to the particular music concentration
7 In addition to the skills required for all 3.07 1 0.01 musicians, functional performance 2 0.01 abilities in keyboard and the voice are 15 0.04 essential. Functional performance 24 0.01 abilities in instruments appropriate to 26 0.00 the student’s teaching specialization 28 0.04 are also essential
8 Knowledge and skills sufficient to 3.05 1 0.01 teach beginning students on 2 0.01 instruments and/or in voice as 24 0.01 appropriate to the chosen areas of 26 0.01 specialization
9 The ability to think, speak, and write 2.92 1 0.00 clearly and effectively, and to 2 0.00 communicate with precision, cogency, and rhetorical force
10 An understanding of and the ability to 2.79 1 0.00 read and realize musical notation 2 0.00 4 0.05
AUTOMATICITY EXPECTATIONS 186
11 Ability to teach music at various 2.79 1 0.00 levels to different age groups and in a 2 0.00 variety of classroom and ensemble settings in ways that develop knowledge of how music works syntactically as a communication medium and developmentally as an agent of civilization. This set of abilities includes effective classroom and rehearsal management
12 The ability to assess aptitudes, 2.75 1 0.00 experiential backgrounds, orientations 2 0.00 of individuals and groups of students, 4 0.01 and the nature of subject matter, and to plan educational programs to meet assessed needs
13 An understanding of evaluative 2.74 1 0.00 techniques and ability to apply them 2 0.00 in assessing both the musical progress 4 0.04 of students and the objectives and procedures of the curriculum
15 Students must acquire basic 2.68 1 0.00 knowledge of music history and 2 0.00 repertories through the present time, 4 0.00 including study and experience of 5 0.01 musical language and achievement in 6 0.05 addition to that of the primary culture 7 0.04 encompassing the area of specialization
17 An acquaintance with a wide selection 2.66 1 0.02 of musical literature, the principal 2 0.02 eras, genres, and cultural sources
18 The ability to place music in 2.66 1 0.02 historical, cultural, and stylistic 2 0.02 contexts
21 While synthesis is a lifetime process, 2.60 1 0.00 by the end of undergraduate study 2 0.00 students must be able to work on 4 0.02 musical problems by combining, as appropriate to the issue, their capabilities in performance; aural, verbal, and visual analysis; composition/improvisation; and history and repertory
AUTOMATICITY EXPECTATIONS 187
22 An understanding of the common 2.57 1 0.00 elements and organizational patterns 2 0.00 of music and their interaction, the 4 0.02 ability to employ this understanding in aural, verbal, and visual analyses, and the ability to take aural dictation
23 Knowledge of content, methodologies, 2.51 1 0.04 philosophies, materials, technologies, 2 0.04 and curriculum development for instrumental music
24 The ability to hear, identify, and work 2.47 1 0.00 conceptually with the elements of 2 0.00 music such as rhythm, melody, 4 0.00 harmony, structure, timbre, texture 5 0.00 6 0.02 7 0.01 8 0.01
25 Keyboard competency 2.45 1 0.00 2 0.00 4 0.00 5 0.01
26 Knowledge and skill in the selected 2.45 1 0.00 area(s) of specialization (composition, 2 0.00 electronic and computer music, ethnic 4 0.00 music, guitar, small ensembles, jazz, 5 0.00 keyboard, orchestral music, music 6 0.01 history and theory, music in 7 0.00 combination with other disciplines, 8 0.01 music technologies, and popular music; or combinations of one or more of these types of content with aspects of the general, vocal/choral, or instrumental specializations) sufficient to teach beginning and intermediate students effectively
27 The ability to use instruments, 2.39 1 0.00 equipment, and technologies 2 0.00 associated with the area(s) of 4 0.01 specialization (composition, electronic 5 0.03 and computer music, ethnic music, guitar, small ensembles, jazz, keyboard, orchestral music, music history and theory, music in combination with other disciplines, music technologies, and popular music; or combinations of one or more of these types of content with aspects of the general, vocal/choral, or instrumental specializations)
AUTOMATICITY EXPECTATIONS 188
28 The prospective music teacher should 2.38 1 0.00 be able to apply analytical and 2 0.00 historical knowledge to curriculum 4 0.00 development, lesson planning, and 5 0.01 daily classroom and performance 7 0.04 activities. Teachers should be prepared to relate their understanding of music with respect to styles, literature, multiple cultural sources, and historical development, both in general and as related to their area(s) of specialization
Correlations. The chapter’s final section analyzes automaticity expectations for possible correlations among participant responses. Although this section does not relate to a specific research question, it explores the possible relationships between findings of the other research questions. Pearson Product Moment Correlations were conducted in order to explore potential relationships between automaticity expectations for corresponding music educator skills and: (a) required courses,
(b) field experiences, and (c) evaluation. The researcher also investigated possible connections between the types of institutions represented, in addition to the teaching experience of music teacher educator participants.
The reason Pearson Product Moment Correlations were computed between courses and music educator skill expectations was more obvious in some instances than in others. Clearly, a relationship might be possible between the number of required theory classes and the expectations for demonstrated theoretical knowledge or teaching skills related to music theory. Other correlations were run between courses that often included certain characteristics that could influence music teacher educators, even if development of those specific skills was not the focus of those courses. For example, pre-service teachers could develop some knowledge and AUTOMATICITY EXPECTATIONS 189 abilities concerning teaching secondary instruments by participating in large ensembles and observing the conductor modeling these skills.
Some statistical significance was indicated, but all correlations ranged from
0.12 to 0.28. This weakness should be taken into consideration when interpreting these results. The correlations found between coursework and automaticity expectations are displayed in Table 4.13. Some apparent correlations were expected, such as the relationship between technology course requirements and technological skill expectations. Yet, one might have anticipated a stronger relationship. Other correlations that were not found were also surprising. A few examples include: (a) the number of music theory courses and skills related to theoretical knowledge, (b) the amount of applied study and primary instrument skills, (c) the number of conducting courses and conducting skills, and (d) playing secondary instruments in laboratory ensembles and secondary instrument skills.
AUTOMATICITY EXPECTATIONS 190
Table 4.13
Significant Correlations between Coursework and Automaticity Expectations
Coursework Skill Expectations Correlation Number of music history Demonstrated historical knowledge r(282) = 0.17, p = .005 classes Teaching skills related to historical r(284) = 0.19, p = .002 knowledge
Technology course Technological skills r(288) = 0.20, p = .001 requirement
Number of semesters in large Primary instrument teaching skills r(290) = 0.13, p = .026 ensembles Secondary instrument teaching skills r(286) = 0.14, p = .020
Conducting skills r(286) = 0.20, p = .001
Classroom management skills r(285) = 0.20, p = .001
Recorder technique class Secondary instrument performance skills r(257) = 0.12, p = .048 requirement Secondary instrument teaching skills r(257) = 0.16, p = .012
Voice technique class Voice performance skills r(284) = 0.21, p = .000 requirement
Piano requirement Piano performance skills r(291) = 0.13, p = .033
Conducting in laboratory band Conducting skills r(239) = 0.17, p = .010
Classroom management skills r(239) = 0.22, p = .001
General rehearsal skills r(239) = 0.19, p = .003
No significant correlations were apparent between the number of music teacher education courses with incorporated teaching opportunities through field experiences and automaticity expectations for most categories of skills. The exceptions were classroom management (r(287) = 0.12, p = .036) and technological skills (r(285) = 0.12, p = .046). There were also no correlations indicated between automaticity expectations and the number of required hours of teaching within field experiences or the length of student teaching. AUTOMATICITY EXPECTATIONS 191
Some correlations were found between automaticity expectations and different types of practice opportunities for teaching skills (see Table 4.14). These practice opportunities included peer teaching, tutoring, assisting in local schools, university- affiliated youth activities and music camps, and service learning. Peer teaching and tutoring showed the most correlations while university-affiliated music camps showed the least.
Table 4.14
Significant Correlations between Practice Activities and Automaticity Expectations
Skills Peer Tutoring Local University University Service Teaching Schools Activities Camps Learning 25 r(296) = 0.24, r(294) = 0.18, r(299) = 0.13, p = .000 p = .002 p = .029
27 r(297) = 0.12, r(295) = 0.16, r(300) = 0.20, p = .047 p = .007 p = .001
29 r(297) = 0.19, r(295) = 0.17, r(300) = 0.14, r(300) = 0.12, p = .001 p = .003 p = .014 p = .048
31 r(296) = 0.25, r(294) = 0.24, r(298) = 0.12, r(299) = 0.14, r(299) = 0.15, p = .000 p = .000 p = .031 p = .019 p = .009
34 r(293) = 0.19, r(291) = 0.18, r(296) = 0.14, p = .001 p = .003 p = .018
35 r(293) = 0.25, r(291) = 0.17, r(296) = 0.18, p = .000 p = .003 p = .002
36 r(292) = 0.28, r(290) = 0.23, r(294) = 0.13, r(295) = 0.15, r(295) = 0.14, p = .000 p = .000 p = .023 p = .012 p = .020
37 r(291) = 0.13, r(289) = 0.20, r(293) = 0.21, r(294) = 0.18, r(292) = 0.13, p = .027 p = .001 p = .000 p = .002 p = .023
Note. Numbers in the skill column refer to survey questions (i.e., 25 = teaching skills related to theoretical knowledge, 27 = teaching skills related to historical knowledge, 29 = primary instrument teaching skills, 31 = secondary instrument teaching skills, 34 = conducting skills, 35 = classroom management skills, 36 = general rehearsal skills, 37 = technological skills).
Correlations were also computed in order to investigate potential relationships between automaticity expectations and types of evaluative tools utilized in represented music teacher education programs. Few significant correlations were AUTOMATICITY EXPECTATIONS 192 indicated (see Table 4.15). No statistically significant relationships were found between automaticity expectations and use of verbal feedback, university-mandated forms, state-mandated forms, portfolios, ePortfolios, and standardized written tests.
Additionally, primary instrument teaching skills and conducting skills appeared to not be correlated with any of the sample evaluative tools. One unanticipated finding was the prevalence of correlations with action research projects, considering participants reported this type of tool to be least used.
Table 4.15
Significant Correlations between Evaluative Tools and Automaticity Expectations
Skills Self Written Checklists Rating Scales Rubrics Action Evaluations Feedback Research Projects 25 r(300) = 0.12, p = .037
27 r(301) = 0.15, r(301) = 0.11, p = .007 p = .035
31 r(300) = 0.12, r(300) = 0.12, r(300) = 0.15, p = .033 p = .044 p = .027
35 r(297) = 0.15, r(297) = 0.14, r(297) = 0.14, r(297) = 0.12, p = .011 p = .017 p = .014 p = .036
36 r(296) = 0.13, r(296) = 0.14, r(296) = 0.12, r(296) = 0.17, r(296) = 0.17, p = .022 p = .019 p = .049 p = .003 p = .004
Note. Numbers in the skill column refer to survey questions (i.e., 25 = teaching skills related to theoretical knowledge, 27 = teaching skills related to historical knowledge, 31 = secondary instrument teaching skills, 35 = classroom management skills, 36 = general rehearsal skills).
Some relationships were indicated between music teacher education programs that used some type of evaluation across multiple courses and automaticity expectations for certain categories of skills, including: (a) teaching skills related to theoretical knowledge (r(251) = 0.14, p = .027), (b) secondary instrument teaching skills (r(250) = 0.15, p = .015), (c) classroom management skills (r(247) = 0.18, AUTOMATICITY EXPECTATIONS 193
p = .005), and (d) general rehearsal skills (r(247) = 0.18, p = .004). Additionally, the amount of university supervision of pre-service teachers during the student teaching practicum appeared to be related to teaching skills involving historical knowledge
(r(286) = 0.15, p = .011) and general rehearsal skills (r(282) = 0.13, p = .029), but no other categories of skills.
Summative correlations were computed in order to determine if there were relationships between overall automaticity expectations and experience of participants, as well as music teacher educators’ types of institutions. A significant relationship was found with music teacher educators’ amount of PK-12 teaching experience (r(301) = 0.19, p = .001) but not with their number of years as music teacher educators. There was also no significant correlation between the type of institution
(i.e., public or private university, conservatory, or community college) and overall expectations of automaticity.
Summary of Results
Data analysis on how music teacher education programs were preparing the skills needed by beginning instrumental music teachers produced many different results. The most frequently required courses were applied lessons and large ensembles. Most programs also required all instrumental music education undergraduates to take piano, brass, woodwind, percussion, string and voice technique classes. Institutions were more likely to offer and require laboratory bands compared to laboratory orchestras. Additionally, laboratory ensembles were usually used to practice conducting skills rather than secondary instrument performance skills. Field experience teaching components were part of two to four classes in most AUTOMATICITY EXPECTATIONS 194 music teacher education programs. Participants reported that pre-service teachers were required to complete an average of 89.61 hours of teaching within field experience teaching activities prior to student teaching. Other sample practice opportunities for teaching skills appeared to be most frequently not required, but also had some undergraduate involvement. Most programs ended in a semester-long student teaching period, after which a PK-12 Music teaching certificate or license was earned.
Evaluation during instrumental music teacher preparation most frequently involved written and verbal feedback, self evaluation, and rubrics. Other evaluative tools mentioned by participants frequently incorporated video. Slightly more than a third of the represented music teacher education programs used evaluative tools across multiple courses. The most prevalent instruments in these cases were rubrics and ePortfolios. Most music teacher educators indicated pre-service teachers were observed by university supervisors three to four times during their student teaching experience.
Statistically significant differences were found between automaticity expectations for teaching and performance skills, with the latter being more expected.
For both varieties, skills associated with one’s primary instrument were most expected. No categories of skills were expected to be completely automatic or nonautomatic. Further analysis showed weak or nonexistent correlations between automaticity expectations and program characteristics and evaluation approaches.
AUTOMATICITY EXPECTATIONS 195
CHAPTER 5
Discussion
A growing number of researchers have focused on the collegiate preparation of music educators, investigating the characteristics of novice and expert teachers, different approaches or practices currently in use, and potential improvements for programs. What can be expected from the products of music teacher education programs, however, has not been as widely examined yet. Several studies discussed the effectiveness of programs based on what beginning music teachers were achieving, but few asked music teacher educators or programs what they expected of their graduates based on the preparation provided. One of the objectives of the present study was to investigate this different perspective. This was accomplished through the lens of automaticity, a psychological principle related to skill acquisition, which had no previous, direct connection with music teacher education. Sources that connected automaticity to teaching in general were reviewed, but often involved broad statements that seemed to indirectly relate to the principle and had to be interpreted by the researcher. The following chapter summarizes the findings of this study in relation to the literature, and presents potential explanations, overall conclusions, possibilities for future research, and implications for music teacher education.
Summary of the Study
The purpose of this study was to examine instrumental music teacher educators’ perceptions regarding the level of automaticity of teaching knowledge and skill expected of beginning instrumental music teachers. By achieving some degree of automaticity on certain music educator skills during undergraduate teacher AUTOMATICITY EXPECTATIONS 196 preparation, beginning teachers might enter their first year with a set of well-practiced teaching and performance skills, thus enabling them to possibly avoid praxis shock, cognitive overload, burnout, and attrition. It is highly unlikely all skills will reach this high level of acquisition by the end of music teacher education programs, but if methods of preparation and evaluation are organized and carried out effectively, some might achieve automaticity and others could grow to be automatic during beginning teachers’ first few years.
A wide variety of topics were covered in the literature review. They were arranged according to the Backward Design approach, involving desired results, evidence of achievement, and methods of attaining the results (Wiggins & McTighe,
2005). The initial section dealt with research on expertise, automaticity, and beginning and expert music educators. This was followed by a concentration on skill acquisition, automaticity’s relationship to neurological activity, and assessment within music teacher education. The last portion of the literature review focused on topics related to undergraduate instrumental music education curricula and experiences.
The researcher-developed online survey instrument was based on the following research questions:
• How were music teacher education programs preparing the music educator
skill set necessary for beginning instrumental music teachers?
• How were music teacher education programs evaluating the music educator
skill set necessary for beginning instrumental music teachers? AUTOMATICITY EXPECTATIONS 197
• What teaching skills did music teacher education programs expect to be
automatic in their graduating instrumental music teachers, as reported by
music teacher educators?
• What performance skills did music teacher education programs expect to be
automatic in their graduating instrumental music teachers, as reported by
music teacher educators?
Respondents provided demographic data, specific information on curricula, field experiences, and evaluations methods of programs, in addition to automaticity expectations for a variety of teaching and performance skills.
Three hundred and three instrumental music teacher educators completed the survey, signifying a 36% response rate. This population had an average of approximately sixteen years experience as music teacher educators. Nearly two-thirds of the participants currently taught at public universities, with the remaining third teaching at private institutions. A smaller number also taught at conservatories and community colleges.
Findings
Music teacher education preparation.
Coursework. The preparation of music educators involves a variety of courses, experiences, and practice opportunities. Statistical analysis of the survey responses revealed the following findings concerning instrumental music teacher education. Courses geared toward performance skills received some of the highest number of required semesters in the overall curriculum (e.g., applied lessons, large ensembles). This appears to coincide with the arguments put forth by Trollinger AUTOMATICITY EXPECTATIONS 198
(2006) and Wiggins (2007), who stressed the importance of performance abilities, not just in those college students who would become professional musicians, but also in those preparing to be music educators. With such frequencies reported, most programs did not agree with Reimer’s (2004) stance that performance studies should be less emphasized in music teacher education.
The debate over whether pre-service music teachers should be prepared to teach all music subjects or certain specializations was partially evident in the responses concerning technique class requirements. For classes directly related to band and orchestra ensembles (brass, woodwind, percussion, and string techniques), the largest number of participants indicated all instrumental music education majors were required to take these. Lending credence to the generalist view, piano study and voice techniques also were required for all instrumental music education majors at many institutions. However, guitar and recorder, which might be more associated with general music, were not required in most programs, indicating some specialization. Overall, programs appeared to prepare more generalists than specialists, which went against the advice of Cutietta (2007), NBPTS (2001), Berliner
(2004), and Colwell (2006).
Laboratory ensemble requirements also supported the generalist perspective.
When responding to laboratory band and orchestra requirements, the most prevalent answers were “not required/not offered” and “required for all instrumental music education students”. The NASM (2010) handbook does not specifically state anything about laboratory ensembles, but it does assert that laboratory experiences in which pre-service teachers can practice rehearsal skills are vital. Overall, findings suggested AUTOMATICITY EXPECTATIONS 199 music teacher education programs were more likely to offer and require a laboratory band but not an orchestra, which might have implications for how prepared beginning instrumental music teachers are for teaching strings. Moreover, laboratory experiences were used more often to practice conducting skills, rather than secondary instrument performance skills.
Additional evidence of the preparation of generalists can be seen in the certification or licensure pre-service teachers received upon completion of their degrees. Most earned a PK-12 Music certificate, qualifying them to teach all musical subjects at all levels. Noticeably fewer pre-service teachers earned a license specifying instrumental music. This was similar to Henry’s (2005) findings on the types of music teacher certification in different states. It probably also reflected accreditation and state department of education requirements.
Other requirements corresponded with those of NASM, which would be expected given that most university music programs are accredited by NASM.
However, beyond the general percentages for different categories of courses (see
Table 1.3), no specific number of classes appeared to be mandated for music education degrees. Therefore, individual programs might determine the extent to which different courses and experiences were offered as long as the broad requirements of NASM, the state departments of education, and other accreditors were met. For example, NASM (2010) indicated all music education programs should include conducting among its courses, but did not give a minimum number of classes or specify achievement of certain benchmarks when developing conducting skills.
The small standard deviation for conducting course requirements in this study’s AUTOMATICITY EXPECTATIONS 200 results suggest a high level of agreement that most programs offered two courses, especially compared to the larger standard deviation and smaller mean indicated for small ensembles.
Field experience. Teaching experience was considered one of the most important components of music teacher education in several studies (Ballantyne,
2007b; Bauer & Berg, 2001; Brophy, 2002; Conway, 2002; Schmidt, 2010; Standley
& Madsen, 1991; Teachout, 1997). Most participants reported their programs included some type of teaching experience in two to four courses and required between zero and six hundred hours (or an average of approximately 90 hours) teaching in field experiences prior to student teaching. This was excessive compared to what Schmidt (1989) found in a survey of undergraduate music education programs (a range of zero to three hundred hours and an average of 66 hours). The wide variety of hours reported might have been indicative of the limited perspective of some faculty, as well as different conceptualizations about “field experience.”
What is considered field experience could vary between programs and even between courses and faculty at the same institution (Wilson et al., 2001).
Music teacher educators were also asked about the offerings, student involvement, and requirements of other potential practice opportunities. Most activities (i.e., tutoring/lessons, assisting in local schools, working with university- affiliated youth activities or music camps, and service learning) were not required, but pre-service teachers were somewhat involved. The exception was peer teaching through courses, which was frequently required with much student involvement.
Music teachers in Brophy’s (2002) study believed peer teaching activities were AUTOMATICITY EXPECTATIONS 201 beneficial in the development of sequencing and planning, but found self-arranged teaching experience, including teaching private lessons, assisting in local schools, and working with String Projects, as even more helpful. Regarding the less prevalent activities indicated in this study, programs may be missing chances to provide additional, valuable practice opportunities. Pre-service teachers could engage in much more deliberate practice, even when some experiences are not expressly aligned with coursework.
Evaluation in music teacher education programs. One way the skill acquisition of pre-service music educators can be understood and tracked is through a variety of evaluation approaches used during music teacher preparation. In analyzing the frequency of evaluative tools used to assess the skills of pre-service music educators, over two-thirds of respondents indicated their programs used written feedback, verbal feedback, self evaluation, and rubrics. While exactly when and how all these techniques were utilized, and what they measured, was beyond the scope of this study, the prevalence of these tools suggested a limited professional consensus.
This partially contradicts Rohwer and Henry’s (2004) conclusion that music teacher education programs did not appear to agree on how pre-service teachers should be evaluated. Furthermore, the variety of tools might indicate pre-service teachers were receiving frequent assessment from several directions in many of the represented programs. It also might suggest multiple types of measures is a good thing in the development of music educator skills.
It was difficult to compare the frequency and variety of evaluative tools used by participants in this study to many of the sources referred to in the literature review AUTOMATICITY EXPECTATIONS 202 because these were often vague about how evaluation was actually occurring. For example, Applegate (1985) described how evaluations were used in coursework, field experiences, and observations of teaching, but only included one specific tool by which this was accomplished (written tests in coursework.) All others were general descriptions about when evaluation took place. Similar dilemmas were seen when trying to compare this study’s findings to those of Rohwer and Henry (2004).
However, some mention was made that skills related to developing goals, learning outcomes, and lesson plans, as well as choosing and arranging materials, were specifically measured. ATE (2000) called for evaluation through several methods during student teaching, specifically mentioning the use of tools such as portfolios, self evaluation, and peer evaluation. NCATE (2008) also stressed that pre-service teachers should be evaluated through several different methods.
The use of evaluation at different times throughout the teacher education program, such as when the pre-service teacher is officially admitted to the education program, at transitions (e.g., before entrance into student teaching), and at the end of the program when student teaching is complete, was also emphasized by NCATE
(2008). Results of these assessments could be very informative to the teacher education program as a way to analyze the effectiveness of the preparation provided.
They could also be used for accountability purposes on both the side of the pre- service teacher and the teacher educator. While this study did not specifically ask about the timing of evaluations, some evidence was discovered in the coding of responses concerning evaluation across programs. Some participants indicated certain assessments were paired with particular academic years. Others revealed evaluation AUTOMATICITY EXPECTATIONS 203 occurred throughout their program, either at the conclusion of each year or course, or developed through different classes. It is likely that any programs that are nationally accredited by NCATE or other teacher education organizations also do this to some extent.
Approximately a third of the programs used some sort of evaluation across multiple courses. The most prevalent tools in these cases were rubrics and ePortfolios and were often used to assess general teaching skills, dispositions, or more specific music educator abilities. NCATE (2008) recommended continuous evaluation that was based on professional, state, and institutional standards. Two suggestions ATE
(2000) supplied for providing effective assessment were use of consistent forms of evaluation throughout the teacher education program and inclusion of a variety of approaches.
Most music teacher educators in this study indicated university supervisors observed pre-service teachers three to four times during their student teaching experience, which was typically a semester in length. This finding stands out against the determination by NCTQ that at least five evaluative observations during student teaching were most effective. This frequency would involve university supervisors evaluating student teachers every two to three weeks (Boyd et al. 2009; Greenberg et al. 2011). Only 33% of this study’s participants reported that university supervisors observed student teachers four or more times.
While there appeared to be no widely accepted measurement tool used among all music teacher education programs, some music teacher educators did indicate they used standardized written tests, such as Praxis or National Teacher Exams. However, AUTOMATICITY EXPECTATIONS 204 this represented less than a third of the programs. Perhaps an instrument like the
Survey of Teacher Effectiveness (Hamann & Baker, 2009) could be further modified for use in assessment of pre-service teachers as they developed their skills. This tool has been used in various forms for several studies (Austin & Miksza, 2012; Butler,
2001; Fant, 1996; Hamann et al., 1998; Paul et al., 2001). Even though it has no obvious connection to automaticity, the Survey of Teacher Effectiveness could be used to communicate progress in overall skill acquisition to pre-service teachers and teacher educators.
Automaticity expectations. The primary purpose of this study was to examine instrumental music teacher educators’ automaticity expectations for beginning instrumental music teachers. Automaticity had not been previously or directly connected to the preparation of music educators, although it appeared, at least in descriptions, within some sources on teacher expertise and among the characteristics of effective teachers (Bergee, 2005; Berliner, 1986, 1988, 2001, 2004;
Bond et al., 2000; Carter, 1987 as cited in Carter et al., 1988; Dewey, 1933; Doyle,
1986; Feldon, 2007a; Hammerness et al., 2005; Leicester, 1990). Therefore, it was difficult to make many connections to related literature in the discussion of results. A limited number of studies presented findings on the abilities of beginning music educators and their preparation, which could be compared to the results of this research. Possible reasons behind the significant findings are also proposed.
Statistically significant differences were found between the expectations of teaching and performance skills, with the latter being slightly more expected. One possible explanation could be music teacher education curriculum. Performance and AUTOMATICITY EXPECTATIONS 205 teaching skills may be taught differently. For example, when pre-service teachers are learning conducting and primary instrument performance skills in conducting courses, applied lessons, and large ensembles, they must physically practice these skills on a consistent basis while in the class. In the case of some teaching skills, knowledge of skills might be introduced in music education coursework but may not be personally practiced until later in the program. Another explanation could be occupational identity. The music teacher educators represented in this study could view beginning teachers more as musicians who teach, as opposed to teachers of music. Thus, they might expect performance skills to be at a higher level of acquisition. Reported expectations, additionally, could reflect the culture of music preparation at the represented institutions. Programs might emphasize one or the other previously mentioned identities, which would then impact the preparation offered and the expectations reported. Finally, the apparent difference between performance and teaching skills was probably not meaningful: The scale used for automaticity expectations was likely not robust enough to differentiate between 2.77 and 2.97.
Primary instrument skills were the most expected categories for both performance and teaching skills. Similar to the explanation in the previous paragraph concerning higher expectations for performance skills, a possible explanation could be the way in which primary instrument skills are taught. The performance variety is the focus of applied lessons and large ensemble courses, which this study found to be required for the most number of semesters. (However, there were surprisingly no correlations between the frequency of these and primary instrument skill expectations.) While primary instrument teaching skills would not necessarily be the AUTOMATICITY EXPECTATIONS 206 focus of coursework that concentrated on performance skills, these courses and instructors might unintentionally influenced their development as well.
Among the top performance skills, seven related to primary instruments. In addition to the possible explanations mentioned previously, primary instrument skills might have been rated higher because undergraduate students entered their programs with some assumed abilities from many prior years of musical experience. However, for the same reason, the fact that primary instrument performance skills were not expected to be completely automatic was difficult to understand. While some participants indicated these skills would be automatic, others ranked them at lower levels, resulting in an average range between 3.57 and 3.82 for those skills included in the top ten. Some conducting skills were also included in the top ten, but these represented basic skills, such as holding the baton and performing patterns in the right hand only.
For the least expected performance skills, three involved more advanced piano performance technique, which might rely on prior automaticity of more basic skills
(i.e., using the pedal while playing, performing simple accompaniments, and playing the melody in the right hand while accompanying with the left hand). Three skills also represented demonstrated theoretical knowledge (notating aural examples, interpreting clefs, and playing transposed parts). The low expectations involving transpositions were surprising consider this would be an essential skill for instrumental music teachers specializing in band. Similarly, interpreting different clefs would be necessary for orchestra teachers. Two of the lowest rated performance skills also dealt with basic repairs on both primary and secondary instruments. Such AUTOMATICITY EXPECTATIONS 207 low expectations might suggest these topics are not addressed in courses in represented music teacher education programs.
Upon examination of the top ten teaching skills, four related to technology and four related to primary instrument skills. For the technology skills, only one might be specific to music education (i.e., use of notation software). The most expected skills (i.e., using communication technology and word processing software) were likely not taught in music teacher education programs, although they were probably used for coursework. Just as pre-service teachers would probably have pre- existing abilities on their primary instruments, they would probably have earlier experience using these technological skills in their daily and academic lives. Primary instrument teaching skills (i.e., modeling and explaining technique, assessing errors, and sequencing beginning instruction) were also among the top teaching skills, possibly because pre-service teachers had already acquired many of their foundational primary instrument performance skills, allowing them to direct more of their attention toward the novel tasks of teaching their instrument.
Even though some of the most expected teaching skills were technological, four of the least expected were also technological (i.e., practicing software, sound equipment, GarageBand, and website construction). This could be a result of generational differences between music teacher educators and pre-service teachers.
Considering participants had been music teacher educators for an average of almost
16 years, they might not have used some of these newer technological skills in an educational setting, and therefore, did not possess the skills themselves. Six of the lowest ranked teaching skills also related to music history or theory knowledge. AUTOMATICITY EXPECTATIONS 208
Participants indicated these subjects were required to varying degrees in their programs. These courses might have provided content knowledge, but perhaps failed to help pre-service teachers connect this knowledge to their developing teaching abilities. Additionally, the two lowest theory skills related to transposition, similar to the finding in performance skills.
No skills were expected to be completely automatic. However, there was an overall high degree of automaticity expected. According to the automaticity expectations scale created for this study (see Table 4.6), most music educator skills were expected to be beginning automatic (performable while occasionally focusing on something else). Primary instrument performing and teaching, conducting, and performing on secondary instruments received overall higher ratings at the approaching automatic level (performable while mostly paying attention to something else), with primary instrument performance skills perceived to be closest to automatic. No categories were expected to be entirely nonautomatic, although one individual skill was ranked at this level (i.e., using website construction software).
Overall expectations might have not only reflected the expected abilities of beginning instrumental music teachers in respect to automaticity. They might also have been representative of music teacher educators’ perceptions of skill complexity and how these skills were addressed in their programs’ curricula.
Correlations. An interesting finding in the current study was the lack of correlations between attributes of music teacher education programs and automaticity expectations. Correlations were computed in order to explore potential relationships between program characteristics reported by music teacher educators and their AUTOMATICITY EXPECTATIONS 209 subsequent expectations for automatic skills among beginning teachers who had gone through their program. These characteristics included coursework, field experience, and evaluation approaches. While statistical significance was indicated in some cases, all correlations were weak, ranging from 0.12 to 0.28.
There could be several possible explanations for such weak or nonexistent correlations. The reported expectations might not have been accurate, but rather were influenced by participant backgrounds, perspectives, or bias. Future research would be needed to determine the plausibility of this explanation. Another explanation could be that music teacher educators might not expect some skills to develop sufficiently until they are required to perform them on a more consistent basis as beginning teachers with their own students. Berliner (2004), Haack (2003), Conway (2003b),
Roulston et al. (2005), and Mark and Madura (2010) made similar statements.
It would have been logical to find correlations between curricular requirements and automaticity expectations, yet few were indicated. Instructional rigor might vary widely between courses, faculty, and programs, which could be a potential reason behind this finding. Accrediting organizations mandate specific standards, competencies and experiences, but the actualization of these are still dependent on individual music teacher educators. For example, one conducting course at a certain institution might provide an equal or more solid foundation to conducting skills than two conducting classes at a different institution. Forsythe et al.
(2007) communicated a similar concern when trying to determine what standards music teacher educators actually considered when developing and teaching their own courses. They could be quite variable depending on how individuals interpreted the AUTOMATICITY EXPECTATIONS 210 standards within their curricular context. An additional limitation related to this explanation could be the way in which information was gathered in the survey instrument. Music teacher educators were asked to indicate the quantity of courses, but not their content or quality. An additional qualitative portion might have facilitated the collection of this more in depth data.
The researcher hypothesized earlier in the study that more opportunities for deliberate practice during music teacher preparation might increase the potential for automaticity. This study’s findings did not appear to support this: Programs that reportedly offered or required more field experience did not necessarily have increased expectations for automaticity in beginning instrumental music educator skills. Possible explanations include: (a) differing concepts about terms used in the survey; (b) the inclusion of all faculty involved in some portion of instrumental music teacher programs, irrespective of their potential limited knowledge of the program as a whole; and (c) similar to the explanation in the previous paragraph, the possibility that field experiences at some institutions were not equivalent to those at other institutions. Given the importance placed on fieldwork in music teacher education, this finding might indicate the necessity to examine what these experiences are actually contributing to pre-service teachers’ skill development, although again, this would likely vary between programs.
Overall, this study’s finding that music teacher education program characteristics had little or no relationship to automaticity expectations of beginning instrumental music teachers might be similar to Bauer and Berg’s (2001) study. They found undergraduate music education coursework was only sometimes influential AUTOMATICITY EXPECTATIONS 211 when instrumental music teachers planned for instruction, implemented learning activities, and assessed student learning. Other factors related to teachers’ current position or mentors (i.e., college ensemble conductor, colleagues, applied instructors, high school conductor) were reported to be more influential.
Further analyses. No statistically significant differences appeared between domains, revised taxonomy levels, or InTASC standards. A slight, significant difference was found between skills identified as content and pedagogical content knowledge. As mentioned previously, the occupational identity music teacher educators projected on their pre-service teachers, either musician or educator, could possibly explain this finding. Music skills might be more expected, rather than their educational application. In addition, several significant differences were apparent between certain NASM competencies. Considering the likelihood that NASM accredited many of the represented institutions in this study, its competencies could have been an influential factor in reported expectations. On the other hand, as seen in this study’s low interrater reliability obtained for the NASM competencies, this finding might be rather suspect since music teacher educators and programs could interpret these quite differently.
Limitations of the Study
Generalizations from the findings of this study should be made cautiously.
While every effort was made to survey a representative population of instrumental music teacher educators, there is no guarantee the responses of these individuals reflect those that would have been reported by the rest of the profession. The faculty who participated in this study had varying levels of involvement in their institutions’ AUTOMATICITY EXPECTATIONS 212 overall music teacher education curriculum. Besides specific music education faculty, participants potentially included applied instructors who were also responsible for teaching instrumental technique classes, as well as ensemble conductors who taught conducting to pre-service teachers. Additionally, the population did not represent 303 distinct programs because the invitation to participate was not limited to one music education faculty member per institution. In order to insure anonymity, it was impossible to identify individuals from their survey responses. While it could be verified that more than one faculty member of a university completed the survey, the researcher would not be able to compare, contrast, or merge their responses.
This study’s findings rely on self-reported data, rather than information that was collected more qualitatively or observationally. Diverse experiences, partial views of the overall program, inaccurate recollection, personal bias, and misrepresentation might have consequently influenced responses. Additionally, the researcher depended on the professional judgment of the participants by not defining qualifiers in the automaticity expectations section.
In regard to the data collected in the survey, some changes are recommended if the instrument is used again for future research:
• Participants were asked to indicate what courses they taught in their PK-12
experience. Many responded with multiple subjects, but it would have been
helpful to know what their specialization had been.
• The researcher sought information on how many courses in these programs
had field experience components in which the pre-service teachers taught.
However, the researcher did not ask specifically about how these experiences AUTOMATICITY EXPECTATIONS 213
aligned with courses, which could have been anything from peer teaching to
actual school teaching. A follow-up question would have clarified this.
• Rather than asking for a categorical response concerning the number of times
student teachers were observed, participants could be asked to supply the
exact number of times. This would have provided more intervallic data, rather
than the nominal data that were produced in this study’s findings.
• Ask participants to identify their state since questions on teacher certification
& program characteristics would likely be representative of state mandates.
• Include peer assessment as another type of evaluative tool.
• Music teacher educators were asked to indicate what skills were expected to
be automatic in the beginning instrumental music teachers that completed
their programs. However, it would have been beneficial to know if they
expected any skills, such as primary instrument performing, to be automatic at
the beginning of the program.
Suggestions for Future Research
The current study is just the beginning of a potentially new way of understanding and evaluating skill acquisition of music educators. Future research through the principle of automaticity would advance the usefulness and comprehension of this study’s initial findings. New studies could compare these findings to those of other populations in order to produce a broader picture of the effectiveness of music teacher education. For example, the automaticity expectations reported in this study could be compared to what beginning teachers themselves perceived they were able to do after their undergraduate preparation. The current AUTOMATICITY EXPECTATIONS 214 survey instrument could be modified in order to reflect automaticity outcomes.
Additionally, this new study could be conducted longitudinally by surveying the same group of teachers a few years later in order to see if their perceptions had changed as they gained more experience. One could also compare music teacher educator expectations to those of cooperating teachers who worked with pre-service teachers at the completion of their programs. The automaticity expectations portion of the survey could remain the same for this population.
This study could also be replicated with different populations entirely. Rather than focusing on instrumental music, researchers could examine vocal or general music specializations, or even subjects beyond music education. Plus, this study could be replicated by reducing the population to different subgroups, such as university supervisors of student teachers, instrumental methods faculty, conducting instructors, or technique class teachers. This could involve several studies in which responses of different populations were eventually compared. One could also replicate the study with instrumental music teacher educators, but gather data qualitatively. The original survey could be used as the basis for an interview instrument, possibly making the questions in the automaticity expectations section more open-ended.
Automaticity could be further explored by comparing novice and expert music educator performances in the classroom, perhaps through video recordings, think aloud approaches, and interviews. Automaticity could also be evaluated through the strategies mentioned by Kraiger et al. (1993): (a) dual-task performance, (b) tasks with interference, and (c) embedded measurements. Finally, a longitudinal approach could be used to study the development of automaticity in beginning music teacher AUTOMATICITY EXPECTATIONS 215 skills, following individuals from their entrance into the music teacher education program through their first few years of teaching.
Conclusions and Implications
The current study examined instrumental music teacher educators’ perceptions regarding the level of automaticity of teaching knowledge and skill expected of beginning instrumental teachers. This was accomplished through an investigation of music teacher education program characteristics and evaluation procedures, as well as automaticity expectations for music educator performance and teaching skills. At the time of this study, there had been minimal research connecting the psychological principle of automaticity to music teacher education, although it had appeared infrequently in studies on teacher expertise. This study may contribute to this connection, while also providing a different way of assessing teacher ability.
Psychological research on automaticity has found that automatic skills might utilize less attention and working memory capacity, thereby making these more available to nonautomatic skills or other necessary foci in the environment.
Attainment of automaticity may also help teachers avoid praxis shock, cognitive overload, burnout, and attrition, which could be especially beneficial for the success of beginning teachers. Teachers may be able to perform automatic skills at a faster pace and with more accuracy and efficiency, while also being able to multi-task. In this study, most music educator skills were expected to be approaching or beginning automatic, which might indicate beginning instrumental music teachers are able to perform a variety of tasks without utilizing all their attention and working memory capacity. Future research would be necessary to determine whether these expectations AUTOMATICITY EXPECTATIONS 216 match the actual abilities of beginning teachers. Furthermore, follow-up studies would need to be conducted to examine if beginning teachers’ automatic skills are, in reality, correlated with fewer occurrences of praxis shock, cognitive overload, burnout, or attrition.
The findings in this study revealed a significant difference between automaticity expectations of performance and teaching skills of beginning instrumental music educators. Higher expectations were indicated for performance skills in general, as well as performing and teaching skills associated with one’s primary instrument. Overall, most music educator skills were at the beginning automatic level, signifying the beginning teacher was expected to be able to perform a skill while occasionally focusing on another task. No skills were expected to be completely automatic or nonautomatic.
Returning to the concept of Backward Design (Wiggins & McTighe, 2005) utilized earlier, if the automaticity expectations reported in this study are indicative of the skills music teacher educators and programs expect of the beginning instrumental music teachers produced in their programs (otherwise known as the desired results), one must consider how programs are evaluating the acquisition of these skills. What evidence would indicate these expectations have been achieved? Once evaluation methods have been determined, what necessary experiences and coursework will help pre-service teachers accomplish the original expectations? One must also reflect on whether the offerings of the program are sufficient to prepare pre-service teachers to achieve the original desired results. Additionally, what other skills do individual AUTOMATICITY EXPECTATIONS 217 programs or teacher educators desire their graduates to possess? This may further determine what is provided to pre-service music teachers.
Some may interpret the current study’s findings as a need for more standardization or accountability in the preparation of music teachers in order to insure the coursework, experiences, and evaluation methods actually contribute to skill acquisition. This is already seen somewhat in certain states’ standardized written assessments of beginning teachers (e.g., Praxis tests), in addition to some structured, but rather flexible guidance from organizations such as NASM, InTASC, NCATE, and ATE. Sogin and Wang (2002) communicated the need for a working model of teacher expertise in the arts, which might help the profession agree on the skills expected to be performable in beginning teachers. Since some viewed automaticity as a potential characteristic of expertise, findings from the current study could contribute to the development of such a model. This could help to further organize teacher preparation and practice opportunities in order to facilitate skill acquisition.
Furthermore, automaticity was believed to be previously, but inadvertently, integrated into music teacher education through the development of conducting abilities (Lonis,
1993). Perhaps a combination of this study’s findings and a future music teacher expertise model could enable automaticity to be taken into account for more music educator skills.
With such weak or nonexistent correlations between automaticity expectations and how pre-service instrumental music teachers are being prepared and evaluated, one might question how much of what is being done in music teacher education really influences the ultimate abilities of beginning teachers. Curricula and experiences of AUTOMATICITY EXPECTATIONS 218 these programs may need to be reviewed. However, these findings only relate to the expectations music teacher educators reported for the beginning teachers produced in their programs. Future research would need to examine the actual automaticity outcomes of beginning music teachers in order to more thoroughly understand the effectiveness of preparation and evaluation approaches. The current study’s focus on automaticity and music teacher education is merely the first step. Some interesting findings were discovered, which will hopefully provide a foundation for future research and contributions to the music teacher education profession.
AUTOMATICITY EXPECTATIONS 219
Appendix A
Table A. Music Educator Skills and Categorizations
Question Skill Domain Type Tax Know InTASC NASM 24. Music Theory Interpret basic C P P 3 C 4 I Conceptual Skills musical terminology while performing on one’s primary instrument
24 Perform notated C P P 3 C 4 I music in differ- ent clefs on one’s primary instrument
24 Perform trans- C P P 3 C 4 I posed music on one’s primary instrument
24 Interpret the C P P 3 C 4 I concert pitch for transposed parts
24 Notate dictated C P P 6 C 4 H, I, T, Y melodic aural examples
24 Notate dictated C P P 6 C 4 H, I, T, Y rhythmic aural examples
24 Perform a C P P 6 C 4 H melody by ear on one’s primary instrument
24 Identify aspects C P 2 C 4 H, I, T, Y of harmony aur- ally (e.g., inter- vals, inversions, chord qualities, cadences)
24 Identify aspects C P 2 C 4 I, T, Y of harmony visu- ally (e.g., inter- vals, inversions, chord qualities, cadences)
AUTOMATICITY EXPECTATIONS 220
24 Identify musical C P 2 C 4 I, T, U, Y forms (e.g., me- lodic phrases, single move- ments, multi- movement works)
25. Music Theory Assess student C T 5 C 6 H, MM, Teaching Skills rhythmic errors QQ quickly and accurately
25 Correct student C P T 5 PC 8 MM, QQ rhythmic errors by modifying instruction and reinforcing corrections
25 Assess transposi- C T 5 C 6 MM, QQ tion errors quickly and accurately
25 Correct trans- C P T 5 PC 8 MM, QQ position errors by giving instruction and reinforcing corrections
25 Sequence C T 6 PC 7,8 DD, FF instruction in order to intro- duce music notation to beginners
25 Sequence C T 6 PC 7,8 DD, FF instruction in order to intro- duce rhythmic counting to beginners
26. Music History Classify different C P 4 C 4 K, V, X, Conceptual Skills historical styles CC from aural examples
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26 Execute C P P 3 C 4 I, X, Y appropriate historical styles or performance practices on one’s primary instrument
26 Identify C P 2 C 4 I, K, V, X historical and musical aspects of composers from different musical eras
26 Identify C P 2 C 4 I, K, U, V, historical forms X characteristic of different musical eras
27. Music History Demonstrate C P P 6 PC 4 H, Y Teaching Skills classification of historical styles from aural examples
27 Sequence C P T 6 PC 7,8 H, CC instruction in order to teach historical styles of performance practices
27 Sequence C P T 6 PC 7,8 H, CC instruction in order to teach historical and musical aspects of composers
27 Sequence skills C P T 6 PC 7,8 H, CC for student comprehension of historical forms
27 Design C T 6 PC 7,8 C, T, CC instruction to connect music with history
28. Primary Hold Instrument P P 3 C 4 M, P, BB, Instrument Properly DD, FF Performance Skills
AUTOMATICITY EXPECTATIONS 222
28 Demonstrate P P 3 C 4 M, P, BB, good posture DD, FF
28 Tune instrument/ C P P 4 C 4 M, P, BB, Match pitch DD, FF
28 Demonstrate C P P 3 C 4 M, P, BB, professional DD, FF sound produc- tion technique
28 Exhibit C P P 3 C 4 M, P, BB, appropriate DD, FF breath support (when applicable)
28 Produce a C P P 3 C 4 M, P, BB, professional- DD, FF level of tone quality
28 Perform with a C P P 3 C 4 M, P, BB, professional- DD, FF level of intonation 28 Demonstrate C P P 3 C 4 I, M, P, proper fingering BB, DD, or performance FF techniques for written notation (when applicable)
28 Justify alternate C P 5 C 4 I, L, BB, fingering options DD, FF (when applicable)
28 Perform articul- C P P 3 C 4 M, P, BB, ation technique DD, FF at a professional- level (bowing, tonguing)
28 Perform basic C P P 3 C 4 repairs
29. Primary Explain basic to CP T 6 PC 7,8 A, BB, Instrument advanced DD, FF Teaching Skills performance technique
AUTOMATICITY EXPECTATIONS 223
29 Model basic to CPA PT 6 PC 7,8 BB, DD, advanced perfor- FF mance tech- nique/music 29 Assess student C T 5 C 6 MM, QQ intonation errors quickly and accurately
29 Assess student C T 5 C 6 MM, QQ technical errors quickly and accurately
29 Correct student C P T 5 PC 7,8 MM, QQ errors by modifying instruction and reinforcing corrections
29 Sequence C T 6 PC 7,8 DD, FF instruction in order to set up a beginner on one’s primary instrument
30. Secondary Assemble C P P 3 C 4 BB, DD, Instrument instruments FF Performance Skills (when applicable)
30 Demonstrate P P 3 C 4 BB, DD, good posture FF
30 Hold instruments P P 3 C 4 BB, DD, properly FF
30 Tune instruments C P P 4 C 4 BB, DD, FF
30 Demonstrate C P P 3 C 4 BB, DD, basic sound FF production technique
30 Use basic breath C P P 3 C 4 BB, DD, support (when FF applicable)
30 Produce a good C P P 3 C 4 BB, DD, tone quality FF AUTOMATICITY EXPECTATIONS 224
30 Demonstrate C P P 3 C 4 I, BB, DD, proper fingering FF or performance technique for basic written notation
30 Demonstrate C P P 3 C 4 BB, DD, good intonation FF
30 Perform basic C P P 3 C 4 BB, DD, articulations FF (staccato, legato, slurs, accents)
30 Perform basic C P P 3 C 4 repairs
31. Secondary Explain basic CP T 6 PC 7,8 A, BB, Instrument performance DD, FF Teaching Skills technique
31 Model basic CPA PT 6 PC 7,8 BB, DD, performance FF technique/music
31 Assess student C T 5 C 6 MM, QQ intonation errors quickly and accurately
31 Assess student C T 5 C 6 MM, QQ technical errors quickly and accurately
31 Correct student C T 5 PC 7,8 MM, QQ errors by modifying instruction and reinforcing corrections
31 Sequence C T 6 PC 7,8 DD, FF instruction in order to set up a beginning instrumentalist
32. Secondary Demonstrate P P 3 C 4 BB, FF Voice Performance good posture Skills
AUTOMATICITY EXPECTATIONS 225
32 Produce a good C P P 3 C 4 BB, FF tone quality
32 Demonstrate C P P 4 C 4 BB, FF ability to match pitch
32 Demonstrate C P P 3 C 4 BB, FF good intonation
32 Perform with C P P 3 C 4 BB, FF good diction
33. Secondary Demonstrate P P 3 C 4 R, BB, FF, Piano Performance good posture HH Skills
33 Perform basic C P P 3 C 4 R, BB, FF, articulations HH (staccato, legato, accents)
33 Use pedal while C P P 3 C 4 R, BB, FF, performing HH
33 Perform basic C P P 3 C 4 R, BB, FF, chord HH progressions (I- IV-V7-I) in most major keys
33 Perform simple C P P 3 C 4 R, BB, FF, accompaniments HH
33 Play melody in C P P 3 C 4 R, BB, FF, right hand and HH harmonized accompaniment in left hand
34. Conducting Hold baton P P 3 C 4 Q, Z Skills properly
34 Perform basic P P 3 C 4 Q, Z conducting patterns (in 1,2,3, 4) clearly in right hand only
AUTOMATICITY EXPECTATIONS 226
34 Perform basic P P 3 C 4 Q, Z conducting patterns (in 1,2,3, and 4) clearly in both hands (mirroring)
34 Perform basic P P 3 C 4 Q, Z subdivisions clearly in right hand only
34 Perform basic P P 3 C 4 Q, Z subdivisions in both hands
34 Maintain steady P P 3 C 4 Q, Z beat while conducting
34 Perform tempo CP P 3 C 4 Q, Z changes while conducting
34 Perform dynamic C P P 3 C 4 Q, Z changes while conducting
34 Perform basic C P P 3 C 4 Q, Z cueing gestures
34 Hold and release P P 3 C 4 Q, Z fermatas
34 Cut off the P P 3 C 4 Q, Z ensemble
35. Classroom Use eye contact CPA T 3 P 3 A, KK Management Skills effectively
35 Give clear CPA T 3 PC 3 A, KK, instructions MM
35 Detect positive C A T 5 P 3 KK and negative student behavior
35 Use extrinsic CPA T 3 P 3 KK motivators
35 Promote intrinsic CPA T 3 P 3 KK motivation
35 Give meaningful, CPA T 5 PC 3,6 A, KK, verbal feedback MM AUTOMATICITY EXPECTATIONS 227
35 Use proximity as CPA T 3 P 3 KK a behavior modification
36. General Devise and carry C P T 6 PC 3,7,8 Q, KK Rehearsal Skills out a warm up routine based on specific technical or musical goals
36 Communicate C P A T 6 P 3,7,8 A, Q, KK the rehearsal plan and goals to students
36 Adjust pacing to C P A T 5 P 1,2,9 Q, GG, meet the needs KK, MM, of the learning PP context
36 Follow an C P T 3 P 3,7,8 Q, KK established classroom routine
36 Adjust lesson C P A T 6 PC 1,2,9 Q, KK, plans to meet the MM, PP needs of the learning context
36 Ask questions C P T 6 PC 5 A, KK that promote critical thinking or higher-order knowledge
36 Include closure C P T 5 PC 6,7 A, Q, KK, at the end of the MM, QQ rehearsal with feedback and plans for future practice
37. Technological Use word C P T 3 P A Skills processing software (e.g., Microsoft Word)
37 Use database C P T 3 P software (e.g., Excel)
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37 Use notation C P T 3 P HH, II, JJ software (e.g., Finale, Sibelius)
37 Use website C P T 3 P A construction software (e.g., Sea Monkey, Adobe Dreamweaver)
37 Use presentation C P T 3 P A software (e.g., PowerPoint, KeyNote)
37 Use practicing C P T 3 P EE, HH, software (e.g., II, JJ Smart Music)
37 Use iTunes C P T 3 P
37 Use GarageBand C P T 3 P HH, JJ
37 Use C P T 3 P A communication technology (e.g., Email, Blogs)
37 Use sound C P T 3 P JJ equipment (e.g., Amps, Microphones, Recording Equipment)
37 Use AV C P T 3 P EE equipment (e.g., DVD/CD Players, Camcorder, Digital Camera)
37 Use office C P T 3 P equipment (e.g., Copier, Scanner, Fax Machine)
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Key for Table A
Column 1. Question from Survey of Automaticity Expectations (see Appendix B)
Column 2. Teaching skill from task analysis.
Column 3. Learning domains of skills. C. Cognitive P. Psychomotor A. Affective
Column 4. Type of music educator skills. T. Teaching P. Performance
Column 5. Identification of skills according to Bloom’s revised taxonomy in the cognitive domain (Anderson et al., 2001). 1. Remembering 2. Understanding 3. Applying 4. Analyzing 5. Evaluating 6. Creating
Column 6. Types of teacher knowledge (Shulman, 1986, 1987). C. Content P. Pedagogical PC. Pedagogical Content
Column 7. InTASC standards (CCSO, 2010) 1. Learner Development. The teacher understands how children learn and develop, recognizing that patterns of learning and development vary individually within and across the cognitive, linguistic, social, emotional, and physical areas, and designs and implements developmentally appropriate and challenging learning experiences. 2. Learning Differences. The teacher uses understanding of individual differences and diverse communities to ensure inclusive learning environments that allow each learner to reach his/her full potential. 3. Learning Environments. The teacher works with learners to create environments that support individual and collaborative learning, encouraging positive social interaction, active engagement in learning, and self-motivation. 4. Content Knowledge. The teacher understands the central concepts, tools of inquiry, and structures of the discipline(s) he or she teaches and creates learning experiences that make these aspects of the discipline accessible and meaningful for learners. AUTOMATICITY EXPECTATIONS 230
5. Innovative Applications of Content. The teacher understands how to connect concepts and use differing perspectives to engage learners in critical/creative thinking and collaborative problem-solving related to authentic local and global issues. 6. Assessment. The teacher understands and uses multiple methods of assessment to engage learners in their own growth, to document learner progress, and to inform the teacher’s ongoing planning and instruction. 7. Planning for Instruction. The teacher draws upon knowledge of content areas, cross-disciplinary skills, learners, the community, and pedagogy to plan instruction that supports every student in meeting rigorous learning goals. 8. Instructional Strategies. The teacher understands and uses a variety of instructional strategies to encourage learners to develop deep understanding of content areas and their connections, and to build skills to access and appropriately apply information. 9. Reflection and Continuous Growth. The teacher is a reflective practitioner who uses evidence to continually evaluate his/her practice, particularly the effects of his/her choices and actions on others (students, families, and other professionals in the learning community), and adapts practice to meet the needs of each learner. 10. Collaboration. The teacher collaborates with students, families, colleagues, other professionals, and community members to share responsibility for student growth and development, learning, and well- being.
Column 8. Select competencies of NASM (NASM, 2010) A. The ability to think, speak, and write clearly and effectively, and to communicate with precision, cogency, and rhetorical force. (VII.D.1.a.1) B. An informed acquaintance with the mathematical and experimental methods of the physical and biological sciences; with the main forms of analysis and the historical and quantitative techniques needed for investigating the workings and developments of modern society. (VII.D.1.a.2) C. An ability to address culture and history from a variety of perspectives. (VII.D.1.a.3) D. Understanding of, and experience in thinking about, moral and ethical problems. (VII.D.1.a.4) E. The ability to respect, understand, and evaluate work in a variety of disciplines. (VII.D.1.a.5) F. The capacity to explain and defend views effectively and rationally. (VII.D.1.a.6) G. Understanding of and experience in one or more art forms other than music. (VII.D.1.a.7)
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H. The ability to hear, identify, and work conceptually with the elements of music such as rhythm, melody, harmony, structure, timbre, texture. (VII.D.2.a.1) I. An understanding of and the ability to read and realize musical notation. (VII.D.2.a.2) J. An understanding of compositional processes, aesthetic properties of style, and the ways these shape and are shaped by artistic and cultural forces. (VII.D.2.a.3) K. An acquaintance with a wide selection of musical literature, the principal eras, genres, and cultural sources. (VII.D.2.a.4) L. The ability to develop and defend musical judgments. (VII.D.2.a.5) M. An overview understanding of the repertory in their major performance area and the ability to perform from a cross-section of that repertory. (VIII.B.1.b) N. The ability to read at sight with fluency demonstrating both general musicianship and, in the major performance area, a level of skill relevant to professional standards appropriate for the particular music concentration. (VIII.B.1.c) P. Technical skills requisite for artistic self-expression in at least one major performance area at a level appropriate for the particular music concentration. (VIII.B.1.a) Q. Knowledge and skills sufficient to work as a leader and in collaboration on matters of musical interpretation. Rehearsal and conducting skills are required as appropriate to the particular music concentration. (VIII.B.1.d) R. Keyboard competency. (VIII.B.1.e) S. Growth in artistry, technical skills, collaborative competence and knowledge of repertory through regular ensemble experiences. Ensembles should be varied both in size and nature. (VIII.B.1.f) T. An understanding of the common elements and organizational patterns of music and their interaction, the ability to employ this understanding in aural, verbal, and visual analyses, and the ability to take aural dictation. (VIII.B.2.a) U. Sufficient understanding of and capability with musical forms, processes, and structures to use this knowledge and skill in compositional, performance, analytical, scholarly, and pedagogical applications according to the requisites of their specializations. (VIII.B.2.b) V. The ability to place music in historical, cultural, and stylistic contexts. (VIII.B.2.c) W. Students must acquire a rudimentary capacity to create original or derivative music. (VIII.B.3) X. Students must acquire basic knowledge of music history and repertories through the present time, including study and experience of musical language and achievement in addition to that of the primary culture encompassing the area of specialization. (VIII.B.4)
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Y. While synthesis is a lifetime process, by the end of undergraduate study students must be able to work on musical problems by combining, as appropriate to the issue, their capabilities in performance; aural, verbal, and visual analysis; composition/improvisation; and history and repertory. (VIII.B.5) Z. The prospective music teacher must be a competent conductor, able to create accurate and musically expressive performances with various types of performing groups and in general classroom situations. (IX.L.3.b.1) AA. The prospective music teacher must be able to arrange and adapt music from a variety of sources to meet the needs and ability levels of individuals, school performing groups, and in classroom situations. (IX.L.3.b.2) BB. In addition to the skills required for all musicians, functional performance abilities in keyboard and the voice are essential. Functional performance abilities in instruments appropriate to the student’s teaching specialization are also essential. (IX.L.3.b.3) CC. The prospective music teacher should be able to apply analytical and historical knowledge to curriculum development, lesson planning, and daily classroom and performance activities. Teachers should be prepared to relate their understanding of music with respect to styles, literature, multiple cultural sources, and historical development, both in general and as related to their area(s) of specialization. (IX.L.3.b.4) DD. Knowledge of and performance ability on wind, string, and percussion instruments sufficient to teach beginning students effectively in groups. (IX.L.3.c.3.a) EE. Knowledge of content, methodologies, philosophies, materials, technologies, and curriculum development for instrumental music. (IX.L.3.c.3.b) FF. Knowledge and skills sufficient to teach beginning students on instruments and/or in voice as appropriate to the chosen areas of specialization. (IX.L.3.c.4.a) GG. The ability to lead performance-based instruction in a variety of settings. (IX.L.3.c.4.e) HH. Knowledge and skill in the selected area(s) of specialization (composition, electronic and computer music, ethnic music, guitar, small ensembles, jazz, keyboard, orchestral music, music history and theory, music in combination with other disciplines, music technologies, and popular music; or combinations of one or more of these types of content with aspects of the general, vocal/choral, or instrumental specializations) sufficient to teach beginning and intermediate students effectively. (IX.L.3.c.5.a) II. Knowledge of content, methodologies, philosophies, materials, technologies, and curriculum development for the area(s) of specialization (see HH). (IX.L.3.c.5.b) AUTOMATICITY EXPECTATIONS 233
JJ. The ability to use instruments, equipment, and technologies associated with the area(s) of specialization (see HH). (IX.L.3.c.5.d) KK. Ability to teach music at various levels to different age groups and in a variety of classroom and ensemble settings in ways that develop knowledge of how music works syntactically as a communication medium and developmentally as an agent of civilization. This set of abilities includes effective classroom and rehearsal management. (IX.L.3.d.1) LL. An understanding of child growth and development and an understanding of principles of learning as they relate to music. (IX.L.3.d.2) MM. The ability to assess aptitudes, experiential backgrounds, orientations of individuals and groups of students, and the nature of subject matter, and to plan educational programs to meet assessed needs. (IX.L.3.d.3) NN. Knowledge of current methods, materials, and repertories available in various fields and levels of music education appropriate to the teaching specialization. (IX.L.3.d.4) PP. The ability to accept, amend, or reject methods and materials based on personal assessment of specific teaching situations. (IX.L.3.d.5) QQ. An understanding of evaluative techniques and ability to apply them in assessing both the musical progress of students and the objectives and procedures of the curriculum. (IX.L.3.d.6)
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Appendix B
Informed Consent Document and Survey of Automaticity Expectations
INFORMED CONSENT DOCUMENT Expectations of Automaticity in Beginning Instrumental Music Educators
You are being asked to participate in a research study about the preparation of beginning instrumental music teachers. You were selected as a participant because of your experience as a music teacher educator. Please read this form before continuing on to the survey.
This research is being conducted through Case Western Reserve University.
Background Information The purpose of this study is to examine instrumental music teacher educators’ perceptions regarding the level of automaticity of teaching knowledge and skill expected of beginning instrumental music teachers.
This study is designed around the following research questions:
1.) How are music teacher education programs PREPARING the music educator skill set necessary for beginning instrumental music teachers?
2.) How are music teacher education programs EVALUATING the music educator skill set necessary for beginning instrumental music teachers?
3.) What TEACHING skills do music teacher education programs expect to be automatic in their graduating instrumental music teachers, as reported by music teacher educators?
4.) What PERFORMANCE skills do music teacher education programs expect to be automatic in their graduating instrumental music teachers, as reported by music teacher educators?
Procedures If you agree to be a participant in this research, we ask you to complete this short survey. Your experience as a music teacher educator will be very helpful.
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Risks and Benefits to Being in the Study There are no foreseeable risks beyond those experienced during the course of daily living.
There are no direct benefits or compensation for participating in this study.
Compensation There will be no compensation for participation in this study.
Confidentiality The records of this research will be kept private. In any sort of report we might publish, we will not include any information that will make it possible to identify a participant or institution. Research records will be kept in a locked file, and access will be limited to the researchers, the University review board responsible for protecting human participants, and regulatory agencies.
Voluntary Nature of the Study Your participation is voluntary. If you choose not to participate, it will not affect your current or future relations with the University or your current employer. There is no penalty or loss of benefits for not participating or for discontinuing your participation. You are free to discontinue participation in this study at any time.
Contacts and Questions The researchers conducting this study are Kathleen Horvath, Ph.D and Amber Peterson. If you have any questions or concerns about this study, you may contact them at [email protected] or (216) 368-1613.
If the researcher cannot be reached, or if you would like to talk to someone other than the researcher(s) about: (1) questions, concerns, or complaints regarding this study, (2) research participant rights, (3) research-related injuries, or (4) other human subjects issues, please contact Case Western Reserve University’s Institutional Review Board at (216) 368-6925 or write: Case Western Reserve University; Institutional Review Board; 10900 Euclid Ave.; Cleveland, OH 44106-7230.
Statement of Consent 1. I have read the preceding information and consent to participate in this study. I verify I am at least 18 years of age. a. Yes b. No
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I. Demographic Information
2. How many years have you been a music teacher educator (teaching music education classes to undergraduate pre-service teachers)? (1-35)
3. How many years were you a music teacher (PK-12) prior to becoming a music teacher educator? (0-35)
4. What did you teach in your prior PK-12 experience? (Check all that apply.) a. Band b. Orchestra c. Choir d. General Music e. Private Lessons f. Other (Please specify in the text box below.)
5. Indicate all degrees you have earned (Check all that apply.) a. Bachelor’s degree in music education b. Bachelor’s degree in performance c. Other bachelor’s degree (Please specify in the text box below.) d. Master’s degree in music education e. Master’s degree in performance f. Master’s degree in performance and pedagogy g. Master’s degree in conducting i. Other master’s degree (Please specify in the text box below.) j. Doctoral degree in music education k. Doctoral degree in performance l. Doctoral degree in conducting m. Other doctoral degree (Please specify in the text box below.)
6. What is the rank of your current position? a. Adjunct Instructor b. Lecturer c. Academic Staff d. Assistant Professor e. Associate Professor f. Professor g. Professor Emeritus h. Other (please specify)
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7. What are the duties of your current position regarding undergraduate students? (Check all that apply.) a. Academic Advisor b. Administrator c. Applied Instructor d. Ensemble Conductor e. Music Teacher Educator (e.g., Foundations, Methods, Techniques, Conducting, Other music education coursework) f. Performer g. Researcher h. University Supervisor of Student Teachers i. Other (please specify)
II. Music Teacher Education Program
8. At what type of institution do you currently teach? a. Community College/University b. Public College/University c. Private College/University d. Music conservatory e. Other (please specify)
9. On what type of scheduling is your institution? a. Quarters b. Trimesters c. Semesters
10. For the following types of subjects, in how many courses are undergraduate instrumental music education students required to enroll? (0-8, Unknown) a. Music Theory b. Music History c. Applied Lessons on primary instrument d. Large Ensembles (e.g., band, orchestra, choir) e. Small Ensembles (e.g., jazz bands, chamber orchestras, quartets) f. Conducting
11. In your program, are undergraduate music education students required to enroll in a music technology course? a. Yes b. No, but this can be taken as an elective. c. No. A music technology course is not offered. d. Unknown
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12. What are your program’s expectations for secondary instrument/voice study in the undergraduate instrumental music education degree? 1 = Not required AND not offered at my institution 2 = Not required BUT offered at my institution 3 = Required for SOME music education students depending on their specialization 4 = Required for ALL instrumental music education students Unknown
a. Brass Techniques b. Woodwind Techniques c. Percussion Techniques d. String Techniques e. Voice Techniques f. Piano (Class or Applied Study) g. Guitar Techniques h. Harp Techniques i. Recorder Techniques
13. Are instrumental music education students required to PERFORM on secondary instruments in laboratory ensembles? 1 = Not required AND not offered at my institution 2 = Not required BUT offered at my institution 3 = Required for SOME instrumental music education students depending on their specialization 4 = Required for ALL instrumental music education students Unknown
a. Lab Band b. Lab Orchestra
14. Are instrumental music education students required to CONDUCT laboratory ensembles prior to the student teaching practicum? 1 = Not required AND not offered at my institution 2 = Not required BUT offered at my institution 3 = Required for SOME instrumental music education students depending on their specialization 4 = Required for ALL instrumental music education students Unknown
a. Lab Band b. Lab Orchestra
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15. With the exception of student teaching, how many courses (offered in the Music Department or Education Department) have a field experience (teaching) component? (0-8 or more, Unknown)
16. Approximately how many hours of field experience (teaching) does your institution require music education students to complete prior to student teaching? (Please indicate an estimated number or "unknown".)
17. In your program, how are undergraduate instrumental music education students involved in the following experiences? 1 = Not applicable. My institution does not offer this experience. 2 = Students have SOME involvement but are NOT REQUIRED to be involved. 3 = Students have SOME involvement and are REQUIRED to be involved. 4 = Students have MUCH involvement but are NOT REQUIRED to be involved. 5 = Students have MUCH involvement and are REQUIRED to be involved. 0 = Unknown
a. Peer teaching through courses b. Tutoring/Private Lessons to PK-12 students c. Assisting in local schools (Marching Band, Sectionals, etc.) d. University-affiliated youth activities (Ensembles, String Projects, Preparatory Divisions) e. University-affiliated music camps f. Service Learning in local schools g. Other (please specify)
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18. What type of evaluative tools are used in your music education program to assess how teaching skills are developing in your undergraduate pre- service teachers? (Check all that apply.) a. Self Evaluation b. Verbal Feedback c. Written Feedback d. University-mandated Forms e. State-mandated Forms f. Checklists g. Rating Scales h. Rubrics i. Portfolios j. ePortfolios k. Action Research Projects l. Standardized Written Tests m. Unknown n. Other (please specify)
19. Does your program use any evaluative tools across multiple music education courses? a. Yes (Please answer Question 20) b. No (Please continue on to Question 21) c. Unknown (Please continue on to Question 21)
20. Briefly describe the evaluative tool(s) used in multiple courses in your music education program.
21. In your program, how long is the student teaching practicum for the undergraduate instrumental music education degree? (Term = quarter, trimester, or semester) a. Less than a term b. One term c. Two terms d. More than two terms e. Unknown f. Other (please specify)
22. How frequently does a university supervisor observe undergraduate student teachers during the practicum period? a. A university supervisor does NOT observe the student teacher. b. 1-2 times per practicum c. 3-4 times per practicum d. More than 4 times per practicum e. Unknown f. Other (please specify)
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23. When instrumental undergraduate music education students complete your program, for which teacher certificate or license will they be qualified? a. PK-12 Music b. PK-12 Instrumental Music c. 6-12 Music d. 6-12 Instrumental Music e. Unknown f. Other (please specify)
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III. Automaticity Expectations
The psychological principle of automaticity is defined as the ability to perform a task with little conscious attention following sufficient practice, thereby allowing one to focus on other tasks.
Teaching is a very complex activity, made up of many diverse skills, functions, and responsibilities to which the teacher must attend at any given moment. If some skills can be executed more automatically, attention may be able to be directed elsewhere, such as to the needs of students, environmental factors in the classroom, teachable moments, and nonautomatic teaching skills.
The following questions concern your music teacher education program’s expectations for the typical beginning instrumental music teacher who has completed your undergraduate music education program. What level are the following music educator skills expected to be at when they finish your program and enter the teaching profession? (“Music educator skills” is an overarching term which subsumes the knowledge and execution of both teaching and performance skills. For the purpose of this study, “teaching skills” refer to pedagogical or pedagogical content understanding demonstrated in instructional abilities, while “performance skills” are associated with physically demonstrated content knowledge (i.e., musical ability or comprehension.)
Please indicate the one level that best represents the skill acquisition of the typical beginning instrumental music teacher who graduates from your program.
1 = The beginning teacher MUST completely focus on this skill while performing it. (Nonautomatic) 2 = The beginning teacher can perform the skill while OCCASIONALLY focusing on something else. (Beginning Automatic) 3 = The beginning teacher can perform the skill while MOSTLY paying attention to something else. (Approaching Automatic) 4 = The beginning teacher can perform the skill while TOTALLY paying attention to something else. (Automatic) 0 = Unknown
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24. When instrumental music education students complete your program, how automatic is their demonstrated theoretical knowledge of music? a. Interpret basic musical terminology while performing on one’s primary instrument b. Perform notated music in different clefs on one’s primary instrument c. Perform transposed music on one’s primary instrument d. Interpret the concert pitch for transposed parts e. Notate dictated melodic aural examples f. Notate dictated rhythmic aural examples g. Perform a melody by ear on one’s primary instrument h. Identify aspects of harmony AURALLY (e.g., intervals, inversions, chord qualities, cadences) i. Identify aspects of harmony VISUALLY (e.g., intervals, inversions, chord qualities, cadences) j. Identify musical forms (e.g., melodic phrases, single movements, multi-movement works)
25. When instrumental music education students complete your program, how automatic are their TEACHING skills related to their theoretical knowledge of music? a. Assess student rhythmic errors quickly and accurately b. Correct student rhythmic errors by modifying instruction and reinforcing corrections c. Assess transposition errors quickly and accurately d. Correct transposition errors by giving instruction and reinforcing corrections e. Sequence instruction in order to introduce music notation to beginners f. Sequence instruction in order to introduce rhythmic counting to beginners
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26. When instrumental music education students complete your program, how automatic is their demonstrated historical knowledge of music? a. Classify different historical styles from aural examples b. Execute appropriate historical styles or performance practices on one’s primary instrument c. Identify historical and musical aspects of composers from different musical eras d. Identify historical forms characteristic of different musical eras
27. When instrumental music education students complete your program, how automatic are their TEACHING skills related to their historical knowledge of music? a. Demonstrate classification of historical styles from aural examples b. Sequence instruction in order to teach historical styles or performance practices c. Sequence instruction in order to teach historical and musical aspects of composers d. Sequence instruction for student comprehension of historical forms e. Design instruction to connect music with history
28. When instrumental music education students complete your program, how automatic are their PERFORMANCE skills on their PRIMARY instrument? a. Hold instrument properly b. Demonstrate good posture c. Tune instrument/Match pitch d. Demonstrate professional sound production technique e. Exhibit appropriate breath support (when applicable) f. Produce a professional-level of tone quality g. Perform with a professional-level of intonation h. Demonstrate proper fingering or performance techniques for written notation (when applicable) i. Use alternate fingering options (when applicable) j. Perform articulation technique at a professional-level (bowing, tonguing) k. Perform basic repairs
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29. When instrumental music education students complete your program, how automatic are their TEACHING skills on their PRIMARY instrument? a. Explain basic to advanced performance technique b. Model basic to advanced performance technique/music c. Assess student intonation errors quickly and accurately d. Assess student technical errors quickly and accurately e. Correct student errors by modifying instruction and reinforcing corrections f. Sequence instruction in order to set up a beginner on one’s primary instrument
30. When instrumental music education students complete your program, how automatic are their PERFORMANCE skills on SECONDARY instruments? a. Assemble instruments (when applicable) b. Demonstrate good posture c. Hold instruments properly d. Tune instruments e. Demonstrate basic sound production technique f. Use basic breath support (when applicable) g. Produce a good tone quality h. Demonstrate proper fingering or performance technique for basic written notation. i. Demonstrate good intonation j. Perform basic articulations (staccato, legato, slurs, accents) k. Perform basic repairs
31. When instrumental music education students complete your program, how automatic are their TEACHING skills on SECONDARY instruments? a. Explain basic performance technique b. Model basic performance technique/music c. Assess student intonation errors quickly and accurately d. Assess student technical errors quickly and accurately e. Correct student errors by modifying instruction and reinforcing corrections f. Sequence instruction in order to set up a beginner on instruments
32. When instrumental music education students complete your program, how automatic are their PERFORMANCE skills on VOICE? a. Demonstrate good posture b. Produce a good tone quality c. Demonstrate ability to match pitch d. Demonstrate good intonation e. Perform with good diction
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33. When instrumental music education students complete your program, how automatic are their PERFORMANCE skills on PIANO? a. Demonstrate good posture a. Demonstrate good posture b. Perform basic articulations (staccato, legato, accents) c. Use pedal while performing d. Perform basic chord progressions (I-IV-V7-I) in most major keys e. Perform simple accompaniments f. Play melody in right hand and harmonized accompaniment in left hand
34. When instrumental music education students complete your program, how automatic are their skills in conducting an instrumental ensemble? a. Hold baton properly b. Perform basic conducting patterns (in 1,2,3, and 4) clearly in right hand only c. Perform basic conducting patterns (in 1,2,3, and 4) clearly in both hands (mirroring d. Perform basic subdivisions clearly in right hand only e. Perform basic subdivisions clearly in both hands f. Maintain steady beat while conducting g. Perform tempo changes while conducting h. Perform dynamic changes while conducting i. Perform basic cueing gestures j. Hold and release fermatas k. Cut off the ensemble
35. When instrumental music education students complete your program, how automatic are their classroom management skills? a. Use eye contact effectively b. Give clear instructions c. Detect positive and negative student behavior d. Use extrinsic motivators e. Promote intrinsic motivation f. Give meaningful, verbal feedback g. Use proximity as a behavior modification
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36. When instrumental music education students complete your program, how automatic are their general rehearsal skills? a. Devise a warm up routine based on specific technical or musical goals b. Communicate the rehearsal plan and goals to students c. Adjust pacing to meet the needs of the learning context d. Follow an established classroom routine e. Adjust lesson plans to meet the needs of the learning context f. Ask questions that promote critical thinking or higher-order knowledge g. Include closure at the end of the rehearsal with feedback and plans for future practice
37. When instrumental music education students complete your program, how automatic are their technological skills? a. Use word processing software (e.g., Microsoft Word) b. Use database software (e.g., Excel) c. Use notation software (e.g., Finale, Sibelius) d. Use website construction software (e.g., Sea Monkey, Adobe Dreamweaver) e. Use presentation software (e.g., PowerPoint, KeyNote) f. Use practicing software (e.g., Smart Music) g. Use iTunes h. Use GarageBand i. Use communication technology (e.g., Email, Blogs) j. Use sound equipment (e.g., Amps, Microphones, Recording Equipment) k. Use AV equipment (e.g., DVD/CD Players, Camcorder, Digital Camera) l. Use office equipment (e.g., Copier, Scanner, Fax Machine)
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Appendix C
Email of Invitation for Study Participation
Dear [FirstName] [LastName],
I am conducting a survey of college faculty as part of my dissertation research on instrumental music teacher education. The purpose of this study is to examine instrumental music teacher educators’ perceptions regarding the level of automaticity of teaching knowledge and skill expected of beginning instrumental music teachers. I obtained your name through The College Music Society.
I am interested in the psychological concept of automaticity, which refers to the ability to perform a task with little concentration. After sufficient learning and practice have occurred, an individual can perform a skill with little conscious effort, thereby allowing attention to be directed elsewhere.
Teaching is a very complex activity, made up of many different skills, functions, and responsibilities to which the teacher must pay attention at any given moment. If some skills can be executed more automatically, attention can be focused on the needs of the students, environmental factors in the classroom, teachable moments, or nonautomatic teaching skills. Additionally, cognitive overload could potentially be avoided if new teachers feel confident in their ability to meet some of the demands of the classroom.
Participation in this study is completely voluntary, but your knowledge and experience as a music teacher educator are very valuable. The survey should take you approximately 15 minutes. Your responses will remain anonymous and data will be generalized for analysis and interpretation.
Please complete the survey by Monday, January 30, 2012. It can accessed at [SurveyLink]. This link is uniquely tied to this survey and your email address. Please do not forward this message. If you would like a copy of the results or have any questions about the survey, please contact me at [email protected].
Thanks for your participation!
Amber Dahlén Peterson Ph.D Music Education candidate Case Western Reserve University
Kathleen Horvath, Ph.D Dissertation Advisor Case Western Reserve University
Please note: If you do not wish to receive further emails, please click the link below, and you will be automatically removed from this mailing list. [RemoveLink]
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Appendix D
Reminder Email for Study Participation
Dear [FirstName] [LastName],
Two weeks ago, you were contacted about participating in a study on instrumental music teacher education. Your perspective is very important. It would be so helpful if you could complete this short survey by midnight, January 30, 2012. The survey can be accessed at [SurveyLink]. This link is uniquely tied to this survey and your email address. Please do not forward this message.
If you have completed the survey already or previously contacted me, thank you and please disregard this message. If you started the survey but have not finished it yet, you can re-enter the survey to complete it.
If you would like a copy of the results or have any questions about the survey, please contact me at [email protected].
Your help is much appreciated!
Amber Dahlén Peterson Ph.D Music Education candidate Case Western Reserve University
Kathleen Horvath, Ph.D Dissertation Advisor Case Western Reserve University
Please note: If you do not wish to receive further emails, please click the link below, and you will be automatically removed from this mailing list. [RemoveLink]
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Appendix E
Final Reminder Email for Study Participation
Dear [FirstName] [LastName],
At the beginning of January, you were contacted about completing a short survey for a dissertation on instrumental music teacher education. Your experience and insight are invaluable, but the survey will only remain open until midnight, January 30. You can access the survey at [SurveyLink]. This link is uniquely tied to this survey and your email address. Please do not forward this message.
If you have completed the survey already or previously contacted me, thank you and please disregard this message.
If you would like a copy of the results or have any questions about the survey, please contact me at [email protected].
Thank you for your assistance!
Amber Dahlén Peterson Ph.D Music Education candidate Case Western Reserve University
Kathleen Horvath, Ph.D Dissertation Advisor Case Western Reserve University
Please note: If you do not wish to receive further emails, please click the link below, and you will be automatically removed from this mailing list. [RemoveLink]
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Appendix F
Table F
Analysis for Possible Correlations between Program Attributes and Music Teacher
Educator Expectations
Program Attributes Expectations (Independent variables) (Dependent variables) Number of courses in music theory Demonstrated theoretical knowledge Teaching theoretical knowledge
Number of courses in music history Demonstrated historical knowledge Teaching historical knowledge
Number of courses in applied lessons Primary instrument performance skills Primary instrument teaching skills
Number of courses in large ensembles Primary instrument performance skills Number of courses in small ensembles Primary instrument teaching skills Secondary instrument teaching skills Conducting skills Classroom management skills General rehearsal skills
Secondary instruments requirements Secondary instrument performance skills Secondary instrument teaching skills
Voice Techniques requirement Vocal performance skills
Piano requirement Piano performance skills
Lab ensemble requirement Conducting skills Number of courses in conducting General rehearsal skills
Technology requirement Technological skills
Field experiences in coursework Teaching theoretical knowledge Number of field experience hours Teaching historical knowledge Other field experiences Primary instrument teaching skills Length of student teaching Secondary instrument teaching skills Conducting skills Classroom management skills General rehearsal skills Technological skills AUTOMATICITY EXPECTATIONS 252
Evaluation methods Teaching theoretical knowledge Number of observations Teaching historical knowledge Primary instrument teaching skills Secondary instrument teaching skills Conducting skills Classroom management skills General rehearsal skills
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Appendix G
Table G
Patterns of Teaching Skill Expectations Across Categories
Teaching Skills Expectations Error detection Assess student rhythmic errors quickly and accurately Assess transposition errors quickly and accurately Assess student intonation errors on primary instrument quickly and accurately Assess student technical errors on primary instrument quickly and accurately Assess student intonation errors on secondary instruments quickly and accurately Assess student technical errors on secondary instruments quickly and accurately
Error correction Correct student rhythmic errors by modifying instruction and reinforcing corrections Correct transposition errors by modifying instruction and reinforcing corrections Correct student errors on primary instrument by modifying instruction and reinforcing corrections Correct student errors on secondary instruments by modifying instruction and reinforcing corrections
Modeling Model basic to advanced performance technique/music on primary instrument Model basic performance technique/music on secondary instruments
Sequencing skills for Sequence skills in order to introduce music notation to beginners beginners Sequence skills in order to introduce rhythmic counting to beginners Sequence skills in order to set up a beginner on primary instrument Sequence skills in order to set up a beginner on secondary instruments
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Communication Use eye contact effectively Give clear instructions Give meaningful, verbal feedback Communicate the rehearsal plan and goals to students Ask questions that promote critical thinking or higher- order knowledge Include closure at the end of the rehearsal with feedback and plans for future practice Use word processing software (e.g., Microsoft Word) Use website construction software (e.g., Sea Monkey, Adobe Dreamweaver) Use presentation software (e.g., PowerPoint, KeyNote) Use communication technology (e.g., Email, Blogs)
Adaptive Expertise Correct student rhythmic errors by modifying instruction and reinforcing corrections Correct transposition errors by modifying instruction and reinforcing corrections Correct student errors on primary instrument by modifying instruction and reinforcing corrections Correct student errors on secondary instruments by modifying instruction and reinforcing corrections Justify alternate fingering options on primary instrument (when applicable) Use proximity as a behavior modification Adjust pacing to meet the needs of the learning context Adjust lesson plans to meet the needs of the learning context
AUTOMATICITY EXPECTATIONS 255
References
Anderson, J.B. & Hill, D. (2001). Principles of good practice for service-learning in
preservice teacher education, In J.B. Anderson, K.J. Swick, & J. Yff (Eds.),
Service learning in teacher education: Enhancing the growth of new teachers,
their students, and communities (pp. 69-84). Washington, D.C.: ERIC
Clearinghouse on Teaching and Teacher Education.
Anderson, J.R. (1982). Acquisition of cognitive skill. Psychological Review, 89(4),
369-406.
Anderson, J.W. (2005). The natural ways of musicality, language, and self-regulation
in infants. Psychomusicology, 19, 41-58.
Anderson, L.W., Krathwohl, D.R., Airasian, P.W., Cruickshank, K.A., Mayer, R.E.,
Pintrich, P.R., Raths, J., & Wittrock, M.C. (Eds.) (2001). A taxonomy for
learning, teaching, and assessing: A revision of Bloom’s taxonomy of
educational objectives. Boston: Allyn & Bacon.
Applegate, J.H. (1985). Early field experiences: Recurring dilemmas. Journal of
Teacher Education, 36, 60-64.
Ashby, F.G., Turner, B.O., & Horvitz, J.C. (2010). Cortical and basal ganglia
contributions to habit learning and automaticity. Trends in Cognitive Sciences,
14(5), 208-215.
Association of Teacher Educators (2000). Standards for field experiences in teacher
education. Reston, VA: Association of Teacher Educators.
AUTOMATICITY EXPECTATIONS 256
Austin, J. R & Miksza, P.J. (2012). Trying on teaching: Effects of a precollegiate
music teacher recruitment program. Journal of Music Teacher Education,
21(2), 14-27.
Ballantyne, J. (2006). Reconceptualising preservice teacher education courses for
music teachers: The importance of pedagogical content knowledge and skills
and professional knowledge and skills. Research Studies in Music Education,
37-50.
Ballantyne, J. (2007a). Documenting praxis shock in early-career Australian music
teachers: The impact of pre-service teacher education. International Journal
of Music Education, 25(3), 181-191.
Ballantyne, J. (2007b). Integration, contextualization, and continuity: Three themes
for the development of effective music teacher education programmes.
International Journal of Music Education, 25(2), 119-136.
Ballantyne, J. & Packer, J. (2004). Effectiveness of pre-service music teacher
education programs: Perceptions of early-career music teachers. Music
Education Research, 6(3), 299-312.
Bargh, J.A. (1989). Conditional automaticity: Varieties of automatic influence on
social perception and cognition. In J. Uleman & J. Bargh (Eds.), Unintended
thought (pp. 1-51). New York: Guilford.
Bargh, J.A. (1992). The ecology of automaticity: Toward establishing the conditions
needed to produce automatic processing effects. The American Journal of
Psychology, 105(2), 181-199.
AUTOMATICITY EXPECTATIONS 257
Bargh, J.A. (1997). The automaticity of everyday life. In R.S. Wyer, Jr. (Ed.),
Advances in Social Cognition, 10, 1-50.
Bargh, J.A. (2007). Social Psychology and the unconscious: The automaticity of
higher mental processes. New York: Psychology Press.
Barnes, G.V. (2002). Opportunities in service learning. Music Educators Journal,
88(4), 42-46.
Bauer, W.I. & Berg, M.H. (2001). Influences on instrumental music teaching.
Bulletin of the Council for Research in Music Education, 150, 53-66.
Baumeister, R.F. (1984). Choking under pressure: Self-consciousness and paradoxical
effects of incentives on skillful performance. Journal of Personality and
Social Psychology, 46(3), 610-620.
Becher, R.M. & Ade, W. (1982). The relationship of field placement characteristics
and students’ potential field performance abilities to clinical experience
performance ratings. Journal of Teacher Education, 33, 24-30.
Beilock, S. (2010). Choke: What the secrets of the brain reveal about getting it right
when you have to. New York: Free Press.
Beilock, S.L. & Carr, T.H. (2001). On the fragility of skilled performance: What
governs choking under pressure? Journal of Experimental Psychology:
General, 130(4), 701-725.
Beilock, S.L., Carr, T.H., McMahon, C., & Starkes, J.L. (2002a). When paying
attention becomes counterproductive: Impact of divided versus skilled-
focused attention on novice and experienced performance of sensorimotor
skills. Journal of Experimental Psychology: Applied, 8(1), 6-16. AUTOMATICITY EXPECTATIONS 258
Beilock, S.L., Wieranga, S.A., & Carr, T.H. (2002b). Expertise, attention, and
memory in sensorimotor skill execution: Impact of novel task constraints on
dual-task performance and episodic memory. The Quarterly Journal of
Experimental Psychology, 55A(4), 1211-1240.
Bereiter, C. & Scardamalia, M. (1993). Surpassing ourselves: An inquiry into the
nature and implications of expertise. Chicago: Open Court Publishing
Company.
Bergee. M.J. (1992). A scale assessing music student teachers’ rehearsal
effectiveness. Journal of Research in Music Education, 40(1), 5-13.
Bergee, M.J. (2005). An exploratory comparison of novice, intermediate, and expert
orchestral conductors. International Journal of Music Education, 23(1), 23-
26.
Berliner, D.C. (1986). In pursuit of the expert pedagogue. Educational Researcher, 5-
13.
Berliner, D.C. (1988, February) The development of expertise in pedagogy. Lecture
at the Annual Meeting f the American Association of Colleges for Teacher
Education, New Orleans, LA.
Berliner, D.C. (2001). Learning about and learning from expert teachers.
International Journal of Educational Research, 35, 463-482.
Berliner, D.C. (2004). Describing the behavior and documenting the
accomplishments of expert teachers. Bulletin of Science, Technology, and
Society, 24(3), 200-212.
AUTOMATICITY EXPECTATIONS 259
Bloom, B.S. (1956). Taxonomy of Educational Objectives, Handbook I: The
Cognitive Domain. New York: David McKay Company, Inc.
Bloom, B.S. (1984, October). Automaticity. Lecture presented to the Graduate
Seminar on Learning, Northwestern University, Evanston, IL. In Lonis, D.J.
(1993). Development and application of a model for the teaching of
conducting gestures. Dissertation Abstracts International. (UMI No.
9314907)
Bloom, B.S. (1986). “The hands and feet of genius”: Automaticity. Educational
Leadership, 43(5), 70-77.
Bond, L., Smith, T., Baker, W.K., & Hattie, J.A. (2000). The certification system of
the national board for professional teaching standards: A construct and
consequential validity study. Greensboro: Center for Educational Research
and Evaluation.
Borko, H. & Livingston, C. (1989). Cognition and improvisation: Differences in
mathematics instruction by expert and novice teachers. American Educational
Research Journal, 26(4), 473-498.
Boyd, D.J., Grossman, P.L., Lankford, H., Loeb, S., & Wyckoff, J. (2009). Teacher
preparation and student achievement. Educational Evaluation and Policy
Analysis, 31(4), 416-440.
Brand, M. (1982). Effects of student teaching on classroom management beliefs and
skills of music student teachers. Journal of Research in Music Education,
30(4), 255-265.
AUTOMATICITY EXPECTATIONS 260
Brand, M. & Burnsed, V. (1981). Music abilities and experiences as predictors of
error-detection skill. Journal of Research in Music Education, 29(2), 91-96.
Bransford, J.D., Brown, A.L. & Cocking, R. (1999). How people learn: Brain, mind,
experience, and school. Washington, D.C.: National Academy Press.
Brophy, T.S. (2002). Teacher reflections on undergraduate music education. Journal
of Music Teacher Education, 12(1), 19-25.
Brown, T.L. & Carr, T.H. (1989). Automaticity in skill acquisition: Mechanisms for
reducing interference in concurrent performance. Journal of Experimental
Psychology: Human Perception and Performance, 15(4), 686-700.
Browning, B. (2007, September). Developing expert-like thinking in pre-service
music teachers. Presentation at the Society for Music Teacher Education
Symposium, University of North Carolina at Greensboro.
Buonomano, D. (2011). Brain bugs: How the brain’s flaws shape our lives. New
York: W.W. Norton & Company.
Burton, S. & Reynolds, A. (2009). Transforming music teacher education through
service learning. Journal of Music Teacher Education, 18(2), 18-33.
Butler, A. (2001). Preservice music teachers’ conceptions of teaching effectiveness,
microteaching experiences, and teaching performance. Journal of Research in
Music Education, 49(3) 258-272.
Byo, J.L. & Cassidy, J.W. (2005). The role of the string project in teacher training
and community music education. Journal of Research in Music Education,
53(4).
AUTOMATICITY EXPECTATIONS 261
Calderhead, J. (1991). The nature and growth of knowledge in student teaching.
Teaching and Teacher Education, 7(5/6), 531-535.
Campitelli, G. & Gobert, F. (2011). Deliberate practice: Necessary but not sufficient.
Current Directions in Psychological Science, 20(5), 280-285.
Carter, K. (1987). Cooperating teachers’ conceptions of teaching. Unpublished paper
prepared for the University of Arizona Cooperating Teacher Project, a project
funded through the office of Educational Research and Improvement, U.S.
Department of Education. In Carter, K., Cushing, K., Sabers, D., Stein, P., &
Berliner, D. (1988). Expert-novice differences in perceiving and processing
visual classroom information. Journal of Teacher Education, 34(3), 25-31.
Carter, K., Cushing, K., Sabers, D., Stein, P., & Berliner, D. (1988). Expert-novice
differences in perceiving and processing visual classroom information.
Journal of Teacher Education, 34(3), 25-31.
Chabris, C. & Simons, D. (2010). The invisible gorilla: And other ways our
intuitions deceive us. New York: Crown.
Chaffin, C.R. (2009). Perceptions of instrumental music teachers regarding the
development of effective rehearsal techniques. Bulletin of the Council for
Research in Music Education, 181, 21-36.
The College Music Society (2010). Directory of music faculties in colleges and
universities, United States and Canada. Missoula, MT: The College Music
Society.
Colwell, R. (2006). Music teacher education in this century: Part II. Arts Education
Policy Review, 108(2), 17-29. AUTOMATICITY EXPECTATIONS 262
Conway, C. (2002). Perceptions of beginning teachers, their mentors, and
administrators regarding preservice music teacher preparation. Journal of
Research in Music Education, 50(1), 20-36.
Conway, C. (2003a). An examination of district-sponsored beginning music teacher
mentor practices. Journal of Research in Music Education, 51(1), 6-21.
Conway, C.M. (2003b). What do we know about beginning music teachers? In C.M.
Conway (Ed.), Great beginnings for music teachers: Mentoring and
supporting new teachers (pp. 3-8). Reston, VA: The National Association for
Music Education.
Council of Chief State School Officers. (2010). Interstate teacher assessment and
support consortium (InTASC) model core teaching standards: A resource for
state dialogue (Draft for Public Comment). Washington, D.C: Council of
Chief State School Officers. Retrieved September 30, 2011 from
http://www.ccsso.org/Documents/2010/Model_Core_Teaching_Standards_D
RAFT_FOR_PUBLIC_COMMENT_2010.pdf
Coyle, D. (2009). The talent code: Greatness isn’t born. It’s grown. Here’s how. New
York: Bantam Books.
Creswell, J.W. (2009). Research design: Qualitative, quantitative, and mixed methods
approaches. Thousand Oaks, CA: SAGE Publications, Inc.
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New
York: HarperCollins Publishers.
AUTOMATICITY EXPECTATIONS 263
Csikszentmihalyi, M. & Nakamura, J. (2010). Effortless attention in everyday life: A
systematic phenomenology. In B. Bruya (Ed.), Effortless attention: A new
perspective in the cognitive science of attention and action (pp. 179-189).
Cambridge, MA: The MIT Press.
Cutietta, R.A. (2000, March). Cloning Holly through field experiences: Do field
experiences help us create the ideal teacher? Paper presented at the meeting
of the Music Educators National Conferences, Washington, DC. In
McDowell, C. (2007). Are they ready to student teach? Reflections from 10
music education majors concerning their three semesters of field experience.
Journal of Music Teacher Education, 16, 45-60.
Cutietta, R.A. (2007). Content for music teacher education in this century. Arts
Education Policy Review, 108(6), 11-18.
Debaere, F., Wenderoth, N., Sunaert, S., Van Hecke, P., & Swinnen, S.P. (2004).
Changes in brain activation during the acquisition of a new bimanual
coordination task. Neuropsychologia, 42, 855-867.
Dehaene, S. (2010). The calculating brain. In Sousa, D.A. (Ed.) Mind, brain, and
education: Neuroscience implications for the classroom (pp. 179-200).
Bloomington, IN: Solution Tree Press. deLeon-Baumann, L. (2009). Behavior management techniques of the master music
teacher. (Unpublished master’s thesis). Miami University, Oxford, Ohio.
DeLorenzo, L.C. (1992). The perceived problems of beginning music teachers.
Bulletin of the Council for Research in Music Education, 113, 9-25.
AUTOMATICITY EXPECTATIONS 264
Denton, J.J. (1982). Early field experience influence on performance in subsequent
coursework. Journal of Teacher Education, 33(2), 19-23.
Detterman, D.K. & Ruthsatz, J. (1999). Toward a more comprehensive theory of
exceptional abilities. Journal for the Education of the Gifted, 22(2), 148-158.
Dewey, J. (1904). The third yearbook of the national society for the study of
education: Part I. The relation of theory to practice in the education of
teachers. Chicago: The University of Chicago Press.
Dewey, J. (1933). How we think: A restatement of the relation of reflective thinking to
the educative process. Boston: D.C. Heath and Company.
Dewey, J. (1938) Experience and education. New York: The MacMillan Company.
Dietrich, A. & Stoll, O. (2010). Effortless attention, hypofrontality, and
perfectionism. In B. Bruya (Ed.), Effortless attention: A new perspective in the
cognitive science of attention and action. (pp. 159-178). Cambridge, MA: The
MIT Press.
Doyle, W. (1986). Classroom organization and management. In M.C. Wittrock (Ed.),
Handbook of research on teaching (pp. 392-425). New York: MacMillan.
Dreyfus, S.E. & Dreyfus, H.L. (1980). A five-stage model of the mental activities
involved in directed skill acquisition. Berkeley, CA: Operations Research
Center.
Duke, R.A. & Simmons, A.L. (2006). The nature of expertise: Narrative descriptions
of 19 common elements observed in the lessons of three renowned artist-
teachers. Bulletin of the Council for Research in Music Education, 170, 7-21.
AUTOMATICITY EXPECTATIONS 265
Dunn, T.G. & Shriner, C. (1999). Deliberate practice in teaching: What teachers do
for self-improvement. Teaching and Teacher Education, 15, 631-651.
Eagleman, D. (2011). Incognito: The secret lives of the brain. New York: Pantheon
Books.
Education.com, Inc. (2006-2011). An education and child development site for
parents: Parenting and Educational Resource. Retrieved March 26, 2011
from http://www.education.com/
Elliott, D.J. (1992). Rethinking music teacher education. Journal of Music Teacher
Education, 1(2), 6-15.
Ely, M.C. & Rashkin, A.E. (2005). Dictionary of music education: A handbook of
terminology. Chicago: GIA Publications, Inc.
Ericsson, K.A. (1996). The acquisition of expert performance: An introduction to
some of the issues. In K.A. Ericsson (Ed.), The road to excellence: The
acquisition of expert performance in arts and sciences, sports, and games (pp.
1-50). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
Ericsson, K.A. (1998). The scientific study of expert levels of performance: General
implications for optimal learning and creativity. High Ability Studies, 9(1), 75-
100.
Ericsson, K.A. (2004). Deliberate practice and the acquisition and maintenance of
expert performance in medicine and related domains. Academic Medicine,
79(10), S70-S81.
Ericsson, K.A. & Charness, N. (1994). Expert performance: Its structure and
acquisition. American Psychologist, 49(8), 725-747. AUTOMATICITY EXPECTATIONS 266
Ericsson, K.A., Krampe, R.T., & Tesch-Römer, C. (1993). The role of deliberate
practice in the acquisition of expert performance. Psychological Review,
100(3), 363-406.
Ericsson, K.A. & Lehmann, A.C. (1996). Expert and exceptional performance:
Evidence of maximal adaptation to task constraints. Annual Review of
Psychology, 47, 273-305.
Ester, D. (2004). Music teacher preparation for the new millennium: A professional
continuum. In E. Pontiff & A. Keating (Eds.), Spotlight on transition to
teaching music: Selected articles from state MEA journals (pp. 35-36).
Reston, VA: MENC.
Ethell, R.G. & McMeniman, M.M. (2000). Unlocking the knowledge in action of an
expert practitioner. Journal of Teacher Education, 51(2), 87-101.
Fant, G.R. (1996). An investigation of the relationships between undergraduate music
education students’ early field experience and student teaching performance.
Unpublished doctoral dissertation, University of Arizona, Tuscon.
Feiman-Nemser, S. & Buchanan, M. (1986). The first year of teacher preparation:
Transition to pedagogical thinking? Journal of Curricular Studies, 18(3), 239-
256.
Feldon, D.F. (2007a). Cognitive load and classroom teaching: The double-edged
sword of automaticity. Educational Psychologist, 42(3), 123-137.
Feldon, D.F. (2007b). The implications of research on expertise for curriculum and
pedagogy. Educational Psychology Review, 19, 91-110.
AUTOMATICITY EXPECTATIONS 267
Ferguson, K. (2003). Becoming a string teacher. Bulletin of the Council for Research
in Music Education, 157, 38-48.
Fitts, P.M. (1964). Perceptual-motor skill learning. In A.W. Melton (Ed.), Categories
of human learning (pp. 243-285). New York: Academic Press.
Fitts, P.M. & Posner, M. I. (1967) Human performance. Belmont, CA: Brooks/Cole
Publishing Company.
Forvilly, J. (2004). Keeping that enthusiasm candle burning brightly. In E. Pontiff &
A. Keating (Eds.), Spotlight on Transition to Teaching Music: Selected
articles from state MEA journals (pp. 137-140). Reston, VA: MENC.
Forsythe, J.L., Kinney, D.W., & Braun, E.L. (2007). Opinions of music teacher
educators and preservice music students on the National Association of
Schools of Music standards for teacher education. Journal of Music Teacher
Education, 19, 19-33.
Gaddis, J. (1992). Application of the Dreyfus Taxonomy to musical conducting.
Dissertation Abstracts International. (UMI No. 9222849.)
Gentner, D.R. (1988). Expertise in typewriting. In M.T.H. Chi, R. Glaser, & M.J.
Farr, (Eds.), The Nature of Expertise (pp. 1-21). Hillsdale, NJ: Lawrence
Erlbaum Associates.
Gladwell, M. (2005). Blink. New York: Back Bay Books.
Gladwell, M. (2008). Outliers: The story of success. New York: Little, Brown, and
Company.
AUTOMATICITY EXPECTATIONS 268
Glaser, R. & Chi, M.T.H (1988). Overview. In M.T.H. Chi, R. Glaser, & M.J. Farr,
(Eds.), The Nature of Expertise (pp. xv-xxviii). Hillsdale, NJ: Lawrence
Erlbaum Associates.
Goolsby, T.W. (1996). Time use in instrumental rehearsals: A comparison of
experienced, novice, and student teachers. Journal of Research in Music
Education, 44(4), 286-303.
Greenberg, J., Pomerance, L. & Walsh, K. (2011). Student teaching in the United
States. Washington, D.C.: National Council on Teacher Quality. Retrieved
July 29, 2011 from www.nctq.org/edschoolreports/studentteaching/report.jsp
Grossman, P., Schoenfield, A., & Lee, C. (2005). Teaching subject matter. In L.
Darling-Hammond & J. Bransford (Eds.), Preparing teachers for a changing
world: What teachers should learn and be able to do (pp. 201-231). San
Francisco: Jossey-Bass.
Haack, P. (2003). Challenges faced by beginning music teachers. In C.M. Conway
(Ed.), Great beginnings for music teachers: Mentoring and supporting new
teachers (pp. 9-24). Reston, VA: The National Association for Music
Education.
Hamann, D.L. (1986a). Burnout and the school orchestra director. Paper presented
for the National School Orchestra Association meeting at the 50th National In-
Service Conference of the Music Educators Conference, Anaheim, California.
In Hamann, D.L., Daugherty, E., & Mills, C.R. (1987). An investigation of
burnout assessment and potential job related variables among public school
music educators. Psychology of Music, 15, 128-140. AUTOMATICITY EXPECTATIONS 269
Hamann, D.L. (1986b). A comparison of MBI burnout levels among public school
music teachers, university instructors, public school orchestra directors, and
public school non-music classroom teachers. Unpublished manuscript.
University of Northern Colorado. School of Music, Department of Music
Education, Greeley, Colorado. In Hamann, D.L., Daugherty, E., & Mills, C.R.
(1987). An investigation of burnout assessment and potential job related
variables among public school music educators. Psychology of Music, 15,
128-140.
Hamann, D.L. & Baker, D. (2009). The Survey of Teaching Effectiveness. In D.L.
Hamann & R. Gillespie, Strategies for teaching strings: Building a successful
string and orchestra program (pp. 248-253). New York: Oxford University
Press. (Reprinted from unpublished manuscript, 1995)
Hamann, D.L., Lineburgh, N., & Paul, S. (1998). Teaching effectiveness and social
skill development. Journal of Research in Music Education, 46(1), 87-101.
Hammerness, K., Darling-Hammond, L. Bransford, J., Berliner, D., Cochran-Smith,
M., McDonald, M., Zeichner, K. (2005). How teachers learn and develop. In
L. Darling-Hammond & J. Bransford (Eds.), Preparing teachers for a
changing world: What teachers should learn and be able to do. (pp. 358-389).
San Francisco, Jessey-Bass.
Haston, W. & Leon-Guerrero, A. (2008). Sources of pedagogical content knowledge:
Reports by preservice instrumental music teachers. Journal of Music Teacher
Education, 17(2), 48-59.
AUTOMATICITY EXPECTATIONS 270
Hatano, G., & Inagaki, K. (1986). Two courses of expertise. In H. Stevenson, H.
Asuma, & K. Hakuta (Eds.) Child Development and Education in Japan (pp.
262-272). San Francisco, CA: Freeman.
Henry, M. (2005). An analysis of certification practices for music educators in the
fifty states. Journal of Music Teacher Education, 14, 47-61.
Hocking, C.A. (1999). Brain electrical activity and automatization. Unpublished
masters thesis. Melbourne: Swinburne University of Technology.
Hogan, T., Rabinowitz, M., & Craven, J.A. (2003). Representation in teaching:
Inferences from research of expert and novice teachers. Educational
Psychologist, 38(4), 235-247.
Hollingsworth, S. (1989). Prior beliefs and cognitive change in learning to teach.
American Educational Research Journal, 26(2), 160-189.
Holt, B.J. & Rainey, S.J. (2002). An overview of automaticity and implications for
training the thinking process. Alexandria, VA: U.S. Army Research Institute
for the Behavioral and Social Sciences.
Hourigan, R.M. & Scheib, J.W. (2009). Inside and outside the undergraduate music
education curriculum: Student teacher perceptions of the value of skills,
abilities, and understandings. Journal of Music Teacher Education, 18(2), 48-
61.
James, W. (1890). The principles of psychology, Volume 1. New York: Henry Holt
and Co. In C.D. Green Classics in the History of Psychology. Retrieved June
22, 2011 from http://psychclassics.yorku.ca/James/Principles/
AUTOMATICITY EXPECTATIONS 271
Jessop, S.G. (2004). Essential characteristics of superior teachers. In E. Pontiff & A.
Keating (Eds.), Spotlight on transition to teaching music: Selected articles
from State MEA journals (pp. 39-40). Reston, VA: MENC.
Kagan, D.M. (1990). Ways of evaluating teacher cognition: Inferences concerning the
goldilocks principle. Review of Educational Research, 60(3), 419-469.
Kelly, L.P. (2003). The importance of processing automaticity and temporary storage
capacity to the differences in comprehension between skilled and less skilled
college-age deaf readers. Journal of Deaf Studies and Deaf Education, 8(3),
230-249.
Kelly, S.N. (2010). Public school supervising teachers’ perceptions of skills and
behaviors necessary in the development of effective music student teachers.
Bulletin of the Council for Research in Music Education, 185, 21-32.
Kiley, K. (2010, November 16). Teacher training should be more practical and
measured better, report says. The Chronicle of Higher Education. Retrieved
November 17, 2010 from http://chronicle.com/article/Teacher-Training-
Should-Be/125407/
Kraiger, K., Ford, J.K., & Salas, E. (1993). Application of cognitive, skill-based, and
affective theories of learning outcomes to new methods of training evaluation.
Journal of Applied Psychology, 78(2), 311-328.
Leedy, P.D. & Ormrod, J.E. (2010). Practical research: Planning and design (9th
ed.). Upper Saddle River, NJ: Pearson Education, Inc.
AUTOMATICITY EXPECTATIONS 272
Lehmann, A.C. & Ericsson, K.A. (1997). Research on expert performance and
deliberate practice: Implications for the education of amateur musicians and
music students. Psychomusicology, 16, 40-58.
Lehmann, A.C. & McArthur, V. (2002). Sight-reading. In R. Parncutt & G.E.
McPherson (Eds.), The science and psychology of music performance (pp.
135-150). New York: Oxford University Press.
Leicester, J.E. (1990). The teachable moment: Expert-novice differences in teachers’
clinical judgments. Dissertation Abstracts International. (UMI No. 9122621.)
Leinhardt, G. & Greeno, J.G. (1986). The cognitive skill of teaching. Journal of
Educational Psychology, 78(2), 75-95.
Lexicon Publications, Inc. (1989). The new lexicon Webster’s dictionary of the
English language. (Encyclopedic ed.). New York: Lexicon Publishers, Inc.
Logan, G.D. (1988). Toward an instance theory of automatization. Psychological
Review, 95(4), 492-527.
Lonis, D.J. (1993). Development and application of a model for the teaching of
conducting gestures. Dissertation Abstracts International. (UMI No.
9314907.)
Marcus, G. (2012). Guitar Zero: The New Musician and the Science of Learning.
New York: The Penguin Press.
Mark, D. (1998). The music teacher’s dilemma – musician or teacher” International
Journal of Music Education, 32, 3-22.
Mark, M. & Madura, P. (2010). Music education in your hands: An introduction for
future teachers. New York: Routledge. AUTOMATICITY EXPECTATIONS 273
Marso, R.N. & Pigge, F.L. (1987). Differences between self-perceived job
expectations and job realities of beginning teachers. Journal of Teacher
Education, 38, 53-56.
Masters, R.S.W. (1992). Knowledge, knerves, and know-how: The role of explicit
versus implicit knowledge in the breakdown of a complex motor skill under
pressure. British Journal of Psychology, 83, 343-358.
McDowell, C. (2007). Are they ready to student teach? Reflections from 10 music
education majors concerning their three semesters of field experience. Journal
of Music Teacher Education, 16, 45-60.
Meinz, E.J. & Hambrick, D.Z. (2010). Deliberate practice is necessary but not
sufficient to explain individual differences in piano sight-reading skill: The
role of working memory capacity. Psychological Science, 21(7), 914-919.
Meske, E.B. (1985). Teacher education: A wedding of theory and practice. Bulletin of
the Council on Research in Music Education, 81, 65-73.
Millican, J.S. (2008). A new framework for music education knowledge and skill.
Journal of Music Teacher Education, 18(1), 67-78.
Millican, J.S. (2009). Band and orchestra teachers’ rankings of general pedagogical
knowledge and skill. Journal of Music Teacher Education, 19(1), 68-79.
Mishra, J. (2005). A theoretical model of musical memorization. Psychomusicology,
19, 75-89.
AUTOMATICITY EXPECTATIONS 274
Moors, A. & Houwer, J.D. (2007). What is automaticity? An analysis of its
component features and their interrelations. In J.A. Bargh (Ed.) Social
Psychology and the unconscious: The automaticity of higher mental processes
(pp. 11-50). New York: Psychology Press.
Müller-Fohrbrodt, G., Cloetta, B., & Dann, H.D. (1978). Der Praxisschock bei
junger Lehrern. Stuttgart: Klett. In Veenman, S. (1984). Perceived problems
of beginning teachers. Review of Educational Research, 54(2), 143-178.
The National Association of Schools of Music. (2010). The National Association of
Schools of Music Handbook. Reston, VA: National Association of Schools of
Music.
The National Board for Professional Teaching Standards (2001). National Board for
Professional Teaching Standards Music Standards. Arlington, VA: National
Board for Professional Teaching Standards.
The National Council for Accreditation of Teacher Education. (2008). Professional
standards for accreditation of teacher preparation institutions. Washington,
D.C.: The National Council for Accreditation of Teacher Education. Retrieved
August 4, 2011 from http://www.ncate.org/LinkClick.aspx?fileticket=
nX43fwKc4Ak%3D&tabid=669
Ormrod, J. (2008). Human Learning. Upper Saddle River, NJ: Prentice Hall.
Palmer, D.J., Stough, L.M., Burdenski, T.K., & Gonzalez, M. (2005). Identifying
teacher expertise: An examination of researchers’ decision making.
Educational Psychologist, 40(1), 13-25.
AUTOMATICITY EXPECTATIONS 275
Patel, V.L., Kaufman, D.R., & Magder, S.A. (1996). The acquisition of medical
expertise in complex dynamic environments. In K.A. Ericsson (Ed.), The road
to excellence: The acquisition of expert performance in arts and sciences,
sports, and games (pp. 1-50). Mahwah, NJ: Lawrence Erlbaum Associates,
Inc.
Paul, S.J., Teachout, D.J., Sullivan, J.M., Kelly, S.N., Bauer, W.I., & Raiber, M.A.
(2001). Authentic-context learning activities in instrumental music teacher
education. Journal of Research in Music Education, 49(2).
Peterson, A.D (n.d.) A comparison of approaches and expectations in string teacher
education. Unpublished manuscript.
Peterson, P.L. & Comeaux, M.A. (1987). Teachers’ schemata for classroom events:
The mental scaffolding of teachers’ thinking during classroom instruction.
Teaching and Teacher Education, 3(4), 319-331.
Poldrack, R.A., Sabb, F.W., Foerde, K., Tom, S.M., Asarnow, R.F., Bookheimer,
S.Y., & Knowlton, B.J. (2005). The neural correlates of motor skill
automaticity. The Journal of Neuroscience, 25(22), 5356-5364.
Polk, J.A. (2006). Traits of effective teachers. Arts Education Policy Review, 107(4),
23-29.
Przygocki, J. (2009). Addressing the string teacher shortage around the country.
American String Teacher. Retrieved February 3, 2010 from
http://www.stringprojects.org/Media_Files/PDFs/NSPCarticle.pdf
AUTOMATICITY EXPECTATIONS 276
Puttemans, V., Wenderoth, N., & Swinnen, S.P. (2005). Changes in brain activation
during the acquisition of a multifrequency bimanual coordination task: From
the cognitive stage to advanced levels of automaticity. The Journal of
Neuroscience, 25(17), 4270-4278.
Reimer, B. (2004). Reconceiving the standards and the school music program. Music
Educators Journal, 91(1), 33-37.
Reynolds, A. (1995). The knowledge base for beginning teachers: Education
professionals’ expectations versus research findings on learning to teach. The
Elementary School Journal, 95(3), 199-221.
Reynolds, A.M. (2004). Service-learning in music teacher education: An overview.
Journal of Music Teacher Education, 13, 9-17.
Reynolds, A.M. & Conway, C.M. (2003). Service-learning in music education
methods: Perceptions of participants. Bulletin of the Council on Research in
Music Education, 1-10.
Richards, C. & Killen, R. (1993). Problems of beginning teachers: Perceptions of pre-
service music teachers. Research Studies in Music Education, 1, 40-51.
Richman, H.B., Gobert, F., Staszewski, J.J., Simon, H.A. (1996). Perceptual and
memory processes in the acquisition of expert performance: The EPAM
model. In K.A. Ericsson (ed.) The Road to excellence: The acquisition of
expert performance in arts and sciences, sports, and games. (pp. 167-187).
Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
AUTOMATICITY EXPECTATIONS 277
Rohwer, D. & Henry, W. (2004). University teachers’ perceptions of requisite skills
and characteristics of effective music teachers. Journal of Music Teacher
Education, 13, 18-27.
Rosenbaum, D.A., Carlson, R.A., & Gilmore, R.O. (2001). Acquisition of intellectual
and perceptual-motor skills. Annual Review of Psychology, 52: 453-470.
Roulston, K., Legette, R., & Womack, S.T. (2005). Beginning music teachers’
perceptions of the transition from university to teaching in schools. Music
Education Research, 7(1), 59-82.
Royer, J.M., Cisero, C.A., & Carlo, M.S. (1993). Techniques and procedures for
assessing cognitive skills. Review of Educational Research, 63(2), 201-243.
Ruthsatz, J., Detterman, D., Griscom, W.S., & Cirullo, B.A. (2008). Becoming an
expert in the musical domain: It takes more than just practice. Intelligence, 36,
330-338.
Ryan, K., Applegate, J., Randall Flora, V., Johnston, J., Lasley, T., Mager, G., &
Newman, K. (1979). My teacher education program? Well. . . : First-year
teachers reflect and react. Journal of Education, 56(4), 267-271.
Sabers, D.S., Cushing, K.S., & Berliner, D.C. (1991). Differences among teachers in
a task characterized by simultaneity, multidimensionality, and immediacy.
American Educational Research Journal, 28(1), 63-88.
Salmon, D.P. & Butters, N. (1995). Neurobiology of skill and habit learning. Current
Opinion in Neurobiology, 5, 184-190.
AUTOMATICITY EXPECTATIONS 278
Schmidt, C.P. (1989). An investigation of undergraduate music education curriculum
content. Bulletin of the Council on Research in Music Education, 99(4), 42-
56.
Schmidt, M. (2005). Preservice string teachers’ lesson-planning processes: An
exploratory study. Journal of Research in Music Education, 53(1). 6-25.
Schmidt, M. (2010). Learning from teaching experience: Dewey’s theory and
preservice teachers’ learning. Journal of Research in Music Education, 58(2),
131-146.
Schneider, W. & Shiffrin, R.M. (1977). Controlled and automatic human information
processing: I. Detection, search, and attention. Psychological Review, 84(1),
1-66.
Schön, D. (1987) Educating the reflective practitioner. San Francisco: Jossey-Bass.
Shavelson, R.J. (1986, June). Interactive decision making: Some thoughts on teacher
cognition. Invited address, I Congreso Internacional, “Pensamientos de los
Profesores y Toma de Decisiones,” Seville, Spain. In Borko, H. & Livingston,
C. (1989). Cognition and improvisation: Differences in mathematics
instruction by expert and novice teachers. American Educational Research
Journal, 26(4), 473-498.
Shiffrin, R.M. (1996). Laboratory experimentation on the genesis of expertise. IN
K.A. Ericsson (ed.) The Road to Excellence: The acquisition of expert
performance in arts and sciences, sports, and games (pp. 337-346). Mahwah,
NJ: Lawrence Erlbaum Associates, Inc.
AUTOMATICITY EXPECTATIONS 279
Shiffrin, R.M. & Schneider, W. (1977). Controlled and automatic human information
processing: II. Perceptual learning, automatic attending, and a general theory.
Psychological Review, 84(2), 127-190.
Shulman, L.S. (1986). Those who understand: Knowledge growth in teaching.
Educational Researcher, 4-14.
Shulman, L.S. (1987). Knowledge and teaching: Foundations of the new reform.
Harvard Educational Review, 1-21.
Siebenaler, D. (2005). Music and service learning: A case study. Journal of Music
Teacher Education, 15, 23-30.
Sogin, D.W. & Wang, C.C. (2002). An exploratory study of music teachers’
perception of factors associated with expertise in music teaching. Journal of
Music Teacher Education, 12, 12-18.
Sousa, D.A. (2010). Mind, brain, and education: Neuroscience implications for the
classroom. Bloomington, IN: Solution Tree Press.
Standley, J.M. & Madsen, C.K. (1991). An observation procedure to differentiate
teaching experience and expertise in music education. Journal of Research in
Music Education, 39(1), 5-11.
Starr, W. & Starr, C. (1983). To learn with love: A companion for Suzuki parents.
Van Nuys, CA: Alfred Publishing Co., Inc.
Steele, N.A. (2010). Three characteristics of effective teachers. Update: Applications
of Research in Music Education, 28(2), 71-78.
AUTOMATICITY EXPECTATIONS 280
Sternberg, R.J. (1996). Costs of expertise. In K.A. Ericsson (ed.), The road to
excellence: The acquisition of expert performance in arts and sciences, sports,
and games (pp. 347-354). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
Sternberg, R.J. & Horvath, J.A. (1995). A prototype view of expert teaching.
Educational Researcher, 24(6), 9-17.
Stokking, K., Leenders, F., De Jong, J., Van Tartwijk, J. (2003). From student to
teacher: Reducing practice shock and early dropout in the teaching profession.
European Journal of Teacher Education, 26(30, 329-350.
Strauch, B. (2010). The secret life of the grown-up brain: The surprising talents of the
middle-aged mind. New York: Penguin Books.
Strayer, D.L. & Kramer, A. F. (1990). An analysis of memory-based theories of
automaticity. Journal of Experimental Psychology: Learning, Memory, and
Cognition, 16(2), 291-304.
Syed, M. (2010). Bounce: Mozart, Federer, Picasso, Beckham, and the science of
success. New York: HarperCollins Publishers.
Teachout, D.J. (1997). Preservice and experienced teachers’ opinions of skills and
behaviors important to successful music teaching. Journal of Research in
Music Education, 45(1), 41-50.
Trollinger, V.L. (2006). A reconception of performance study in the philosophy of
music education. Philosophy of Music Education Review, 14(2), 193-208.
VanLehn, K. (1996). Cognitive skill acquisition. Annual Review of Psychology, 47,
513-539.
AUTOMATICITY EXPECTATIONS 281
Veenman, S. (1984). Perceived problems of beginning teachers. Review of
Educational Research, 54(2), 143-178.
Verducci, S. & Pope, D. (2001). Rationales for integrating service-learning in teacher
education. In J.B. Anderson, K.J. Swick, & J. Yff (Eds.), Service learning in
teacher education: Enhancing the growth of new teachers, their students, and
communities (pp. 2-18). Washington D.C.: ERIC Clearinghouse on Teaching
and Teacher Education.
Wan, C. Y. & Huon, G.F. (2005). Performance degradation under pressure in music:
An examination of attentional processes. Psychology of Music, 33(2), 155-
172.
Weinstein, C.S. (1988). Preservice teachers’ expectations about the first year of
teaching. Teaching and Teacher Education, 4(1), 31-40.
Weinstein, C.S. (1989). Teacher education students’ preconceptions of teaching.
Journal of Teacher Education, 40, 53-60.
Welker, R. (1991). Expertise and the teacher as expert: Rethinking a questionable
metaphor. American Educational Research Journal, 28(1), 19-35.
Westerman, D.A. (1991). Expert and novice teacher decision making. Journal of
Teacher Education, 42(4), 292-305.
Wiggins, G. & McTighe, J. (2005). Understanding by design. Upper Saddle River,
NJ: Pearson Education, Inc.
Wiggins, J. (2007). Authentic practice and process in music teacher education. Music
Educators Journal, 93(3), 36-42.
AUTOMATICITY EXPECTATIONS 282
Wilson, F.R. (1989). Acquisition and loss of skilled movement in musicians.
Seminars in Neurology, 9(2), 146-151.
Wilson, S.M. & Floden, R.E. (2003). Creating effective teachers: Concise answers for
hard questions. An addendum to the report Teacher preparation research:
Current knowledge, gaps, and recommendations. Washington, D.C.: AACTE
Publications.
Wilson, S.M., Floden, R.E., & Ferrini-Mundy, J. (2001). Teacher preparation
research: Current knowledge, gaps, and recommendations. Washington,
D.C.: Office for Educational Research and Improvement.
Wulf, G., Shea, C., & Lewthwaite, R. (2010). Motor skill learning and performance:
A review of influential factors. Medical Education, 44, 75-84.
Yourn, B.R. (2000). Learning to teach: Perspectives from beginning music teachers.
Music Education Research, 2(2), 181-192.
Zeichner, K.M. (1980). Myths and realities: Field-based experiences in preservice
teacher education. Journal of Teacher Education, 31, 45-55.
Zull, J.E. (2002). The art of changing the brain: Enriching the practice of teaching by
exploring the biology of learning. Sterling, VA: Stylus Publishing, LLC.
Zull, J.E. (2011). From brain to mind: Using neuroscience to guide change in
education. Sterling, VA: Stylus Publishing, LLC.