THE IMPACT OF THE EMATH MODEL ON

MATHEMATICS ACHIEVEMENT OF THIRD GRADE STUDENTS

by

VICKI MOORE-ROGERS

(Under the Direction of C. Kenneth Tanner)

ABSTRACT

This study explored the results of a first-year pilot program of a professional development model designed for elementary mathematics teachers. The study examined third grade achievement scores of those students whose teachers participated in the program for the first year. This program modeled specific strategies for targeting mathematics computation skills with technology tools.

The eMath professional development model, consisting of 30 hours of professional development, provided an instructional framework for teachers to develop and use multiple assessment strategies integrated throughout their lesson plans. The model had four major foci: creating an engaged learning environment, data-driven decisions (action research), content enhancement, and collaborative learning communities.

The following question guided this research study: Did students whose teachers participated in the eMath professional development model perform statistically significantly better on standardized tests than students whose teachers did not participate in the eMath professional development model?

Third grade students in elementary schools from two public school systems in Central

Georgia served as the sample for this study. The research design included a posttest-only control

group design with an experimental group of 232 third grade students taught by eMath trained teachers. The control group consisted of 218 third grade students whose teachers were not trained in eMath techniques. These classes were located in comparable Title I elementary schools.

The independent variable in this study was teacher participation or non-participation in the eMath professional development model. The unit of analysis were the CRCT scores (totals and subtotals) of those students whose teachers participated in the training. The dependent variables in this study were CRCT mathematics scores for third grade students (totals and subtotals).

After the first year of implementation, it was found that students whose teachers participated in the eMath professional development model did not perform statistically significantly better on the Criterion-Referenced Competency Test (CRCT) than students whose teachers did not participate in the eMath model. In fact, students whose teachers employed traditional instructional practices performed significantly better on six out of seven categories than those students with eMath teachers. This finding led to the question: How many hours of training are necessary to show improvement?

INDEX WORDS: technology, elementary mathematics, student achievement

THE IMPACT OF THE EMATH MODEL ON

MATHEMATICS ACHIEVEMENT OF THIRD GRADE STUDENTS

by

VICKI MOORE-ROGERS

B.S., Georgia College & State University, 1982

M.Ed., Georgia College & State University, 1984

A Dissertation Submitted to the Graduate Faculty of The University of Georgia in Partial

Fulfillment of the Requirements for the Degree

DOCTOR OF EDUCATION

ATHENS, GEORGIA

2005

© 2005

Vicki Moore-Rogers

All Rights Reserved

THE IMPACT OF THE EMATH MODEL ON

MATHEMATICS ACHIEVEMENT OF THIRD GRADE STUDENTS

by

VICKI MOORE-ROGERS

Major Professor: C. Kenneth Tanner

Committee: C. Thomas Holmes William W. Swan

Electronic Version Approved:

Maureen Grasso Dean of the Graduate School The University of Georgia August 2005

DEDICATION

This dissertation is dedicated to my family. Each member played a significant role in my completing it; and without them I know I could not have kept my sanity. I believe that a good marriage and a strong family is the origin of joy. My husband, Danny, has been my rock, best friend, and source of balance. He has consistently helped me keep perspective on what is important in our lives and how to deal with reality. His unconditional love and his belief in my success have made its completion worthwhile. My son, Matthew, was my joy and inspiration.

My mother, Janette, supported and encouraged me throughout my studies. My sister, Debbie, believed in me and kept me grounded. My dad, “Pep,” would be thrilled to know that I completed this journey. I’ll be looking toward heaven when I cross the stage with the anticipation of his presence.

iv

ACKNOWLEDGEMENTS

The writing of a dissertation can be an isolated and lonely experience. It would not be possible without the personal and practical support of several people. I would be remiss not to acknowledge those who have provided guidance and assistance to me throughout my educational career. You played a significant role in this journey, and I am grateful for you.

Ms. Caroline Lundy, Dr. Andrea Hardin, and Dr. Kaye Bloodworth contributed to my decision to become a teacher. They not only modeled the role of teacher with dignity and professionalism, but they were loving sculptors in the process.

Dr. Ken Tanner convinced me to enter this program and provided his expert guidance during my study. Not only was he readily available when I needed him, but he always read and responded to emails and drafts of my work.

Friends are precious gifts from God, and He has blessed me with an amazing group of people with which to work. The staff at the Macon State College ETTC made my professional life a joy. Thank you all for caring and supporting me.

The “gangster chicks” in my doctoral cohort will always hold a special place in my heart.

v

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS...... v

LIST OF TABLES...... viii

LIST OF FIGURES ...... ix

CHAPTER

1 OVERVIEW OF THE STUDY...... 1

Introduction and Rationale ...... 1

Statement of the Problem ...... 3

Statement of the Purpose...... 3

Significance of the Study ...... 4

Scope and Limitations of the Study ...... 5

Research Design...... 6

Organization of the Study...... 6

2 REVIEW OF THE RELATED LITERATURE ...... 7

Technology Availability in Schools...... 8

Essential Conditions for Successful Technology Integration...... 10

Impact of Technology Integration...... 14

The Benefits and the Challenges...... 23

Summary ...... 31

3 DESIGN OF THE STUDY...... 33

vi

Research Question...... 33

Population and Sample...... 33

Instrumentation...... 35

Research Design...... 36

Statistical Treatment...... 38

Summary ...... 38

4 PRESENTATION AND ANALYSIS OF DATA ...... 39

Data Collection...... 39

Student Achievement ...... 40

5 SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS...... 48

Conclusions ...... 49

Lessons Learned and Recommendations ...... 49

REFERENCES ...... 56

vii

LIST OF TABLES

Page

Table 2.1: Contrasting Views of Instruction and Construction ...... 16

Table 2.2: Harvest Park Middle School Laptop Immersion Program by Grade...... 20

Table 2.3: Harvest Park Middle School Laptop Immersion Program Cohort Design by Academic

Year ...... 21

Table 3.1: Demographic Data for Experimental and Control Schools ...... 35

Table 3.2: Georgia Criterion Referenced Competency Test Alpha Values...... 37

Table 4.1: Descriptive Statistics of Groups (ne=232, nc=218)...... 41

Table 4.2: Differences Between Groups in Seven Mathematics Subscores (N=450) ...... 42

Table 4.3: Student Demographic Information (N=450) ...... 43

Table 4.4: Student Distribution by Age ...... 43

Table 4.5: Differences Between Schools 6 and 7 ...... 44

Table 4.6: Tests of Between-Subjects Effects (Mathematics Total)...... 45

Table 4.7: Demographic Information for Gender and Age (All Students)(n=245) ...... 46

Table 4.8: Tests of Between-Subjects Effects (Mathematics Total)...... 46

Table 4.9: Descriptive Statistics for Gender (Mathematics Total) ...... 47

viii

LIST OF FIGURES

Page

Figure 1: Graphic Representation of eMath Professional Development Model...... 4

ix

CHAPTER 1

OVERVIEW OF THE STUDY

Introduction and Rationale

Current research reveals statistically significant relationships between academic

achievement and the way technology (e.g., computers) is used in the classroom (CEO Forum on

Education and Technology, 2001; The Concord Consortium, 2003; Sivin-Kachala & Bialo,

2000). Research has indicated that the teacher’s role in technology practices in the instructional process has a significant impact on student achievement. For example, Wenglinsky (1998) found that eighth graders whose teachers used computers with higher-order thinking skill activities performed statistically significantly higher on the National Assessment of Education Practices

(NAEP) than those students whose teachers did not use computers with higher-order thinking skill activities. Further, Wenglinksy found that students whose teachers used computers mostly for drill and practice—those activities associated with lower-order thinking skills—performed statistically significantly lower on standardized tests than those students whose teachers who used computers with higher-order thinking skill activities. Fourth grade students were found to score higher on the NAEP if their teachers used computers for teaching mathematics than did students whose teachers did not use computers for teaching mathematics. In both grades, students whose teachers had experienced professional development in computer integration

outperformed students whose teachers did not participate in formal professional development in

computer integration (Wenglinsky, 1998).

1

In the descriptive large scale statewide study in Idaho, researchers used surveys and the

analysis of existing data by the Idaho Council for Technology in Learning (ICTL). The study was designed to determine if increased exposure to technology in the core subjects would positively impact student achievement on the Iowa Test of Basic Skills (ITBS) and the Test of

Achievement and Proficiency (TAP). The ICTL compared the test results from 1994 and 1998 for 26,122 students with high, medium, or low exposure to technology (Idaho Department of

Education, 2001). The level of exposure was determined by student response to a survey. The

sample included one cohort of elementary students and one cohort of secondary students. The

study was a report on the Idaho Technology Initiative (ITI), a five-year, $52,000,000 program

designed to purchase and integrate technology into K-12 classrooms in Idaho.

The student survey was developed to measure the technology exposure students had

experienced over the period under study and was developed using International Society for

Technology in Education (ISTE) National Education Technology Standards (NETS) for students

(ISTE, 1998/2000). A group of Idaho technology experts was assembled, including regional

technology advisors from colleges and universities, parents, teachers, state officials, and students to use the NETS objectives to create a self-reporting student exposure questionnaire which was

used to measure the amount of perceived exposure to technology a student had between the years

1994-1998.

The study documented the impact of the infusion of $52 million for the integration of

technology into district school programs across the state. Researchers found that educational

technology was associated with small but practically significant (effect sizes ranging from .10 to

.24) improvements on standardized test scores in the areas of language, mathematics, reading,

and core studies (Idaho Department of Education, 2001).

2

The eMath professional development model was developed at the Educational

Technology Training Center at Macon State College. Developed under the direction of the

Georgia Department of Education (GaDOE) for implementing the Title IID competitive grant,

this program modeled specific strategies for targeting mathematics computation skills with

technology tools.

The eMath model provided the instructional framework for teachers to develop and

embrace multiple assessment strategies that were integrated throughout their teaching units and

linked to problems or tasks. The model reinforced important complex thinking skills, including

problem solving, decision making, scientific inquiry, and reasoning. Based on the National

Council for Teachers of Mathematics Process and Content Standards and the Georgia

Performance Standards, eMath had four major foci: creating and maintaining an engaged learning environment, data-driven decisions (action research), content enhancement, and

collaborative learning communities. Using a combination of face-to-face and online training

sessions, teachers were active participants in the project beginning with the academic year 03-04.

Figure 1.1 provides a graphic representation of the professional development model.

Statement of the Problem

Mathematics is a critical skill in the information age. Currently, only 25% of third grade

students are performing at or above proficient levels in mathematics. At the fourth grade level,

mathematics achievement in Georgia continues to fall significantly below the national average.

Statement of the Purpose

The purpose of this study was to assess the eMath model’s impact on third grade

mathematics achievement scores on the Criterion-Referenced Competency Test (CRCT). The

following research question was examined: Did students whose teachers participated in the

3

eMath professional development model perform statistically significantly better on standardized

tests than students whose teachers did not participate in the eMath professional development

model?

Figure 1

Graphic Representation of eMath Professional Development Model

Significance of the Study

Technology and its integration are important for school improvement. Boards of education across America are becoming increasingly aware that the No Child Left Behind Act of

2001 (NCLB 2001) requires that technology be integrated into curriculum and instruction in ways that will result in improved student learning. Dollars appropriated through this federal act directly to the states require that local school systems fully integrate a technology-enhanced

4

curriculum (NCLB, 2001). Mathematics scores in Georgia, specifically grades three to five, have been targeted as a priority for the Georgia Board of Education.

Scope and Limitations of the Study

In spite of the efforts to gather complete information, the study has several limitations.

1. Lack of longitudinal perspective. Schools change over time. Some innovations progressively

gain steam as teachers and students come to value them and as they work out the bugs in

implementing them. Others begin with fanfare but gradually fade away because problems in

implementation are not addressed. This study attempts to understand the impact of

technology in the present, but does not systematically review the progressive impact of the

technology over a period of years. However, continuing collection of data by the school

could be used in future years to assess trends and technology's impact across those years.

2. Lack of systematic performance measures. An obvious and serious limitation in the present

study is its lack of direct pre-test measures of student achievement. Only the post-test results

were analyzed. Even so, there are many problems in using them as a basis for isolating

specific effects of technology.

3. Lack of implementation performance measures. Another serious limitation in this study is the

lack of accountability for the teachers’ implementation of the instructional practice and

technology experiences. While classroom observations were conducted, there were no

specific measures for implementing the innovations or administrative support for ensuring

that the innovations were implemented at all.

4. Measuring the achievement of third grade students is difficult because they may not be

“ready” for testing.

5

Research Design

The research design included a posttest-only control group design (Campbell & Stanley,

1963). The experimental group consisted of 232 third grade students taught by eMath trained teachers. These classes were located in Title I elementary schools that were randomly chosen by the GaDOE. The control group consisted of 218 third grade students whose teachers were not trained in eMath techniques. These classes were located in comparable Title I elementary schools that were randomly chosen by the GaDOE.

The independent variable in this study was participation or non-participation in the eMath

professional development model. All teachers addressed the curriculum objectives as identified

in the Georgia Quality Core Curriculum for third grade mathematics. The unit of analysis was the CRCT score (totals and subtotals) of those students whose teachers participated in the

training. A one-way ANOVA was used to test the null hypothesis that there was no statistically

significant difference on 2004 CRCT scores among third grade students whose teachers were

trained or not trained in eMath during the 2003-2004 school year. The dependent variables in this

study were CRCT mathematics scores for third grade students (totals and subtotals).

Organization of the Study

Chapter One presented the introduction, the purpose of the study, the research questions,

and the significance of the study. Chapter Two includes a review of the related literature including specific studies related to technology use in mathematics. Chapter Three describes the criteria for participant selection, methodology, and the statistical treatment used to analyze the data collected by the researcher. Chapter Four reports the findings of the study. Chapter Five summarizes the findings, conclusions, and recommendations to be considered for future research in the area of mathematics professional development with technology.

6

CHAPTER 2

REVIEW OF THE RELATED LITERATURE

Schools today face ever-increasing demands in their attempts to ensure that all students are well-equipped to enter the work place and navigate a complex world. Research has indicated

that technology can support and enhance learning and that it is especially useful in developing higher-order skills of critical thinking, analysis, investigation, and inquiry. The presence of technology in the classroom, however, does not assure that it will be used effectively. Certain essential conditions must be addressed before even the most promising applications and practices

can be implemented successfully. This chapter highlights the literature related to technology

availability in the schools, essential conditions for successful technology integration, the impact

of technology integration on student achievement, and the benefits and challenges of technology

implementation.

Technology integration, for the purposes of this study, is defined as an instructional

program that focuses on student achievement with technology woven into and throughout the

curriculum. It is a program where teachers have immediate access to the technologies they need

and are supported at all levels of expertise by technical and instructional specialists. Brooks-

Young (2002) suggested that technology integration is an environment “where simple

competence is not enough, but where all educators are encouraged to approach problem solving

using a range of thinking skills and learning styles and where teachers ultimately change their

approach to instruction through the use of technology” (p. 46). Effective integration of

technology is achieved when students are able to select appropriate technology tools to help them

7

obtain information in a timely manner, analyze and synthesize the information, and present it effectively. The technology should become an integral part of the classroom’s operations and should be as accessible as all other classroom tools (International Society for Technology in

Education, 1998/2000).

Technology Availability in Schools

Boards of education across America are becoming increasingly aware that the No Child

Left Behind Act (NCLB, 2001) requires that technology be integrated into curriculum and instruction in ways that will result in improved student learning. Dollars appropriated through this federal act are directed to the states that are accountable for fully integrated technology- enhanced curriculum at the district level.

Substantial dollars have been invested in educational technology. For example, Quality

Education Data reported in their 2002 report that spending for technology in 2002 exceeded seven billion dollars (Quality Education Data, 2002). The NCLB Act of 2001 authorized $26 billion for education overall in 2002-2003 and $785 million of these federal funds were allocated specifically for K-12 technology. An additional $2.25 billion is contributed annually from E-rate funding (Market Data Retrieval, 2002).

The Digest of Education Statistics (National Center for Education Statistics, 2000) reported that the percent of students using computers at school more than doubled between 1984 and 1997. Education Week notes that along with Australia, the United States led the world in the number of students per computer, with a ratio of five to one in 2003 (Technology Counts, 2004).

Education Week also noted that 98 percent of our nation’s schools have used laptop computers as instructional tools. Furthermore, 38 states have standards for teacher certification that include

8

technology, 15 states required technology training or coursework for teacher licensure, and 9

states required a technology test for teacher licensure (Technology Counts, 2004).

The E-Rate program is administered by the Federal Communications Commission and seeks to improve technology access by providing schools and libraries with 20% to 90% discounts on qualifying telecommunications services. The discounts are based on the percentage of students eligible for participation in the National School Lunch program. The E-Rate program supports the acquisition of technology infrastructure, Internet and web site services, and the purchase and installation of network equipment and services. Schools are advised to give priority

to upgrading existing hardware and infrastructure before costly data are compromised (U.S.

Department of Education, 2003).

Bushweller (2005) reported that states are spending millions of dollars to build and

maintain powerful data-management systems in order to report student-achievement data as

required by the No Child Left Behind Act of 2001. Bushweller reported that in 15 states, the

NCLB legislation had influenced decision makers to invest in more powerful, robust data

management rather than technology for instructional purposes. He reported that in a recent survey for the Education Week Research Center, it was revealed that 16 states consider data management one of the top two priorities for technology spending. Bushweller observed that

under the Bush administration, the emphasis on technology has shifted to its use as a tool for

analyzing achievement data.

Access to technology, however, will not ensure its use. According to the National Center

for Education Statistics (NCES), access to technology and the Internet has grown significantly

(NCES, 2003). In the fall of 2002, 99% of public schools in the U.S. had access to the Internet.

Public schools have made consistent progress in expanding access in instructional rooms

9

(classrooms, computer and other labs, library/media centers, and any other rooms used for

instructional purposes) from 3% in 1994 to 77% in 2000 and 92% in 2002. More than half (55%) of teachers surveyed in a national study reported being frequent users of technology for

instructional purposes (defined as engaging in at least one computer-related activity a week), with 37% of teachers reporting infrequent use of technology for this purpose, and 8% reporting no use (U. S. Department of Education, 2003).

Essential Conditions for Successful Technology Integration

Most education leaders believe that certain conditions are necessary for schools to

effectively use technology for learning, teaching, and educational management. Physical, human,

financial, and policy issues affect the success of technology use in schools (International Society

for Technology in Education [ISTE] 1998/2000). According to ISTE, there are essential

conditions that must be present in order to support engaged learning environments that promote

effective uses of technology. The essential conditions identified by ISTE (1998/2000) are:

• Vision with support and proactive leadership from the education system

• Educators skilled in the use of technology for learning

• Content standards and curriculum resources

• Student-centered approaches to learning

• Assessment of the effectiveness of technology for learning

• Access to contemporary technologies, software, and telecommunications networks

• Technical assistance for maintaining and using technology resources

• Community partners who provide expertise, support, and real-life interactions

• Ongoing financial support for sustained technology use

• Policies and standards supporting new learning environments (p. 4)

10

Researchers have found that schools must commit to ongoing professional development

for the use of technology in the classroom to make any significant difference in student achievement. For example, in a survey conducted by the New York City-based Center for

Children and Technology (CCT), researchers discovered that the most significant progress in integrating technology into the curriculum was among those systems that had made a commitment to ongoing professional development for their teachers (Harrington-Lueker, 1997).

The systems had employed various strategies to insure quality training, including adding professional development days to the school calendar, developing mentor teachers to assist as trainers, providing teachers with computers at home, and insuring sufficient technical support on site. The professional development centered around pedagogical plans—not mastery of hardware and software.

The CEO Forum on Education and Technology (2001) stated that educational technology can produce significant results in student achievement when applied to clearly defined curriculum standards and integrated into the curriculum by trained teachers. They reported that longitudinal research results in Idaho and West Virginia have shown a statistically significant increase in test scores across subjects and disciplines. Statewide implementations of technology with large-scale curriculum integration garnered an across-the-board increase in all basic skills areas. The same group reported that in a four-year study in Union City, New Jersey, randomly selected students demonstrated significant gains on the SAT among those participating in a four- year technology-integrated curriculum as compared to those students who did not participate in a technology-integrated curriculum. Those students who participated in the technology-integrated curriculum scored 54 points higher in verbal and 34 points higher in mathematics than those

11

students who did not participate in the curriculum (CEO Forum on Education and Technology,

2001).

Johnson and Liu’s (2000) integration model was based on a meta-analysis of 102 case studies of technology integration. They examined 67 cases from the K-12 environment, 24 from higher education teacher training settings, and 11 from in-service training settings. All grade levels and subject areas were included. They found that there were six instructional components that were common to all settings: use of software, use of Web-based instruction, use of Web information resources, use of problem-based learning, instructional design choice, and tailoring multimedia courseware. Their model focused on teachers working in a standards-based environment and using the National Educational Technology Standards (NETS) for students to describe how students used technology for productivity. Johnson and Liu (2000) presented their model as a first step for identifying effective technology integration.

In the descriptive large scale study that was conducted in Idaho, researchers used surveys and the analysis of existing data provided by the Idaho Council for Technology in Learning

(ICTL). The study was designed to determine if increased exposure to technology in the core subjects would impact student achievement on the Iowa Test of Basic Skills (ITBS) and the Test of Achievement and Proficiency (TAP). The ICTL compared the test results from 1994 and 1998 for 26,122 students with high, medium, or low exposure to technology. A student survey was used to determine the level of exposure. The study documented the impact of the infusion of $54 million for the planned integration of technology into district school programs across the state.

Researchers found that educational technology was associated with small but statistically significant improvements on standardized test scores in the areas of language, mathematics, reading and core studies. The range of improvement was from 10% to 24% overall gains in

12

student test scores. The technology factors most closely associated with improved test scores were a student’s ability to select the appropriate software for a project, amount of home and school computer use, amount of Internet and email use, use of technology for class projects, and use of computer simulation programs (Idaho Department of Education, 2001).

As reported by Koehler and Mishra (2005), teacher knowledge for technology integration triangulates between the three factors of content, pedagogy, and technology. The authors reported that in order to go beyond simple skills instruction, technology must be taught in contexts that honor the connections between the technology, the content, and the instruction.

They believed that teachers must participate in design—building something that is sensitive to the subject matter and specific instructional goals. They argued that every act of design should always be a process of weaving together the components of technology, content, and pedagogy.

In a report that evaluated the results of over 300 studies of technology use, the authors concluded that teacher training was the single most significant factor influencing the effective use of educational technology to improve student achievement (Sivin-Kachala & Bialo, 2000).

The report indicated that students of teachers with more than ten hours of training significantly outperformed students of teachers with five or fewer training hours.

International studies have indicated that eighth grade students rank below the international average in mathematics. They found that student achievement may be directly linked with teacher quality, and U. S. teachers do not receive as much practical training and daily support as their German and Japanese colleagues. The Third International Mathematics and

Science Study (TIMSS) found that teachers in the U. S. spend more time in front of the classroom and don’t have adequate time to prepare for instruction (Checkley, 1997). The TIMSS research revealed that although the U.S. education system allows for a great diversity of the

13

teaching of mathematics, educators often fail to coordinate their efforts. The fragmented curriculum in the U.S. has been just one reason cited for the inability of students to explore mathematics topics in depth (Checkley, 1997). Japanese teachers collaborate to develop demonstration lessons before they’re introduced into the classroom. The lessons are taught by one teacher and observed by the rest of the team. After debriefing, modifications are made before the rest of the team implements the lesson (Richardson, 2004).

Impact of Technology Integration

The Apple Classrooms of Tomorrow (1995) research project (ACOT) was implemented to discover if there was a relationship between technology and achievement. Initiated in 1985, the ACOT research continues today, expanding its current work into new areas and continuing to question the impact of technology on teaching and learning (ACOT, 1995). By the end of the first year, students’ behavior and attendance improved significantly, along with their attitudes toward learning as measured using five different approaches: 1) responses to a nationally normed measure, the School Attitude Measure (SAM), for grades 4-8; the Self-Concept and Motivation

Inventory (SCAMIN), for grades K-2; 2) ratings of students’ attitudes toward computers in their persuasive essays on a computer topic; 3) responses of teachers to questionnaire and interview items concerning their perceptions of students’ attitudes; 4) responses of parents to questionnaire and interview items concerning their perceptions of students’ attitudes; and 5) examination of student attendance and mobility patterns for sites that provided the data. Performance significantly improved in test scores, writing competency, and completion of coursework.

Researchers found that students in the ACOT classrooms collaborated more than in traditional classrooms, and the students found that technology was more interesting as they began to use it for creating and communicating as measured by data composed of personal reports from

14

teachers, weekly site reports, classroom observations, interviews with students, parents and teachers, and cross-site assessment data provided by the districts (ACOT, 1995). During the ten year study, the ACOT researchers found that students who continued were developing other competencies that were not usually measured. ACOT students:

• Explored and represented information dynamically and in many forms

• Became socially aware and more confident

• Communicated effectively about complex processes

• Used technology routinely and appropriately

• Became independent learners and self-starters

• Knew their areas of expertise and shared that expertise spontaneously

• Worked well collaboratively

• Developed a positive orientation to the future (Sandholtz, et al., 1997, p. 21).

The ACOT researchers found that a dramatic shift occurred in classrooms as the ACOT teachers extended their traditional styles of teaching and learning—from instruction to knowledge construction. In the early 1990s, educators and groups such as the National Council for Teachers of Mathematics (NCTM) and the National Science Teachers Association (NSTA), agreed to move toward more constructivist learning strategies that called for the teaching of basic skills within authentic context. They believed that by modeling expert thought processes and providing for collaboration and external support, students would achieve intellectual accomplishments that they would not have done on their own (Means, Blando, Olson, &

Middleton, 1993). Sandholtz, Ringstaff, and Dwyer (1997) compared the traditional teaching approach (instruction) to the constructivist teaching approach (construction). That comparison is presented in Table 2.1.

15

The more advanced uses of technology support the constructivist view of learning in which the teacher is a facilitator of learning rather than the only source of knowledge in the classroom. In technology-rich classrooms, students become more engaged and more active learners, and there is typically a greater emphasis on inquiry and less on drill and practice

(Sandholtz, et al., 1997). According to Means, et al. (1993), technology supports the changes in content, roles, organizational climate, and affect that are at the heart of constructivist educational reform movements.

Table 2.1

Contrasting Views of Instruction and Construction

Instruction Construction

Activity Tacher-centered earner-centered Didactic Interactive

Teacher role Fact teller Collaborator always expert sometimes learner

Student role Listener Collaborator Always learner sometimes expert

Learning emphasis Facts Relationships Memorization Inquiry and investigation

Concept of knowledge Accumulation of facts Transformation of facts

Demonstration of success Quantity Quality of understanding

Assessment Norm-referenced Criterion-referenced Portfolios and performances

Technology use Drill and practice Communication, collaboration, information access, and expression (Sandholtz, et al., 1997, p. 14).

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The ACOT study found that teachers progress through stages as they learn how to

integrate technology in the classroom environment. Those stages were identified as the entry, adoption, adaptation, appropriation, and invention stages. As the teachers in the study exhibited

movement through the stages of integration, the researchers observed significantly more

participation in team teaching, interdisciplinary project-based instruction, and the blending of

separate class periods and separate subjects into a full day. As this level of collaboration among

teachers increased, the teachers developed a framework for change. The Nashville ACOT site

designed professional development programs to provide teachers learning opportunities about

integrating technology at the core of instruction. Following two years of this pilot program, the

ACOT Teacher Development Center project was funded by the National Science Foundation in

partnership with ACOT and the school districts (ACOT, 1995).

The skills that the ACOT students developed as a consequence were similar to those

argued for by the U. S. Department of Labor (Secretary’s Commission on Achieving Necessary

Skills [SCANS], 1991). According to the Commission, in addition to basic language and

computational literacy, high school graduates must master the abilities to work with others;

locate, evaluate and use information; organize resources; understand complex work systems; and

work with a variety of technologies.

Research at Harvard’s Educational Technology Center (ETC) focused on targets of

difficulty in mathematics and science (Harrington-Lueker, 1997). The ETC researchers designed

a computer-based classroom unit that used hardware, software, and other equipment to allow

students to experience difficult concepts through a simulation application. After using the model

with 11th-grade students, the researchers compared a group of students taught with the

technology model to a group taught using traditional methods. Both groups spent the same

17

amount of time on the same topics. Interviews with students indicated that those students who learned in a technology-integrated environment had a better understanding of the more difficult concepts than those students who learned with traditional methods. They had fewer misconceptions about the concepts and had a greater ability to discuss distinctions between them.

The National Council of Teachers of Mathematics (NCTM) published their position statement concerning the use of technology in the learning and teaching of mathematics in

October 2003 (NCTM, 2003). This council stated that technology is “essential in teaching and learning mathematics; it influences the mathematics that is taught and enhances students’ learning” (p. 3). NCTM outlined in its recommendations that every school mathematics program should provide students and teachers with the access to technology tools as well as professional development in the use of instructional technology. NCTM recommended that technology integration should occur every day in instruction and should be used at all levels in lesson development and assessment (NCTM, 2003)

In a study conducted by Holmes and Brown (2003), the researchers found that teachers who employed the use of Accelerated Math™ and STAR™ Math software in their classrooms to target the state mathematics curriculum had positive results among their students’ CRCT test results. The researchers found that when the previous year’s test results served as a covariate, the test scores indicated that those students’ test scores were statistically significantly higher than the students in the control schools. Further, they reported that the gains of the first year were maintained and increased in the second year (Holmes & Brown, 2003).

In a study conducted at Harvest Park Middle School in Pleasanton, California, researchers sought to find answers to the following research questions:

1. Does the laptop program have an impact on students’ grade point averages?

18

2. Does the laptop program have an impact on students’ end-of-course grades?

3. Does the laptop program have an impact on students’ essay writing skills?

4. Does the laptop program have an impact on students’ standardized test scores? (Gulek

& Demirtas, 2005).

In this study, the researchers examined the overall grade point averages (GPAs), end-of- course test grades, writing assessment results for the sixth and eighth grade students, standardized test scores, norm-referenced test scores, and the California Standards Tests in language arts and mathematics (Gulek & Demirtas, 2005). Students were issued laptops (either through purchase or through the school’s loaner program) for the year. The students received hands-on training in the basics of computer operation prior to the beginning of the school year.

Students then used the laptops daily during school for various tasks, including: essay writing and

online grading, research on the Web, developing PowerPoint presentations, developing websites, accessing web-based projects and other activities, note taking, and designing posters and logos.

The program starting in 2001 with sixth grade students, but was quickly expanded to include seventh and eighth grade students. As students became more interested in the program, the enrollment increased to 259 students by the year 2003-2004. Table 2.2 summarizes the enrollment in the program and the school for 2003-2004.

The researchers found the demographic composition of students enrolled in the program closely mirrored those of the entire school population. The data analysis was conducted by using descriptive statistics, inferential statistics, and model-based longitudinal analysis. Their findings indicated that the students in the laptop program attained higher GPAs than non-participating students in their respective grades. When examining end-of-course test grades, the researchers found that a higher percentage of laptop students attained A grades and a significantly lower

19

Table 2.2

Harvest Park Middle School Laptop Immersion Program by Grade

Laptop Program Total School Grade Enrollment Enrollment

6 91 353

7 93 361

8 75 371

Total 259 1085

percentage attained F grades in language arts and mathematics courses. It was found that a higher

percentage of laptop students (95% in Grade 6; 91% in Grade 8) met or exceeded grade level

expectation in writing compared to school-wide averages (84% in Grade 6; 83% in Grade 8).

Similar results were found when researchers examined the California Achievement Test (CAT).

A higher proportion of laptop students scores at or above the national average in both the

language and mathematics portions of the CAT across all grade levels (Gulek & Demirtas,

2005).

In an effort to address the issue of prior differences of students enrolled in the laptop program and how those differences contributed to the total difference in student performance as a group, the researchers compared student-learning outcomes from the previous year to the students’ current participation in the program. Using a cohort design, students were tracked in order to compare their achievement outcomes prior to, and at the end of, their first, second, and third years of enrollment in the laptop program. Their cohort design is detailed in Table 2.3. Four separate analyses were conducted for each of the longitudinal mathematics, language, and overall

GPA scores. The results of this analysis indicated that laptop enrollment had a significant effect

20

on mathematics and language scores. Participation in the laptop program was associated with an average per student gain of 16 points for mathematics and 13 points for language scores obtained from the state-mandated standardized tests. Their results also indicated that laptop enrollment yielded a .40 increase in mathematics cumulative GPA and .34 increase in the overall cumulative

GPA scores of participating students.

Table 2.3

Harvest Park Middle School Laptop Immersion Program Cohort Design by Academic Year

Academic Year Baseline Data Year 1 Data Year 2 Data Years 3 Data (Grade 5) (Grade 6) (Grade 7) (Grade 8)

2000-01 Cohort 1 ------

2001-02 Cohort 2 Cohort 1 -- --

2002-03 Cohort 3 Cohort 2 Cohort 1 --

2003-04 -- Cohort 3 Cohort 2 Cohort 1

The clearest evidence on the impact of appropriate use of technology on student learning was reported by Wenglinsky, who analyzed data from the 1996 National Assessment for

Educational Progress (NAEP) in mathematics (Wenglinsky, 1998). Wenglinsky assessed the effects of simulation and higher order thinking technologies on a national sample of 6,227 fourth graders and 7,146 eighth graders mathematics achievement on the National Assessment of

Educational Progress (NAEP). Wenglinsky controlled for socioeconomic status, class size, and teacher characteristics. Thus, the relationships between technology and educational outcomes reported represent the value added by technology for comparable groups of students with comparable teachers in comparable class sizes. Wenglinsky found that eighth-grade students who used simulation and higher order thinking software showed gains in mathematics scores of .42 of

21

a grade level as measured by NAEP. Further, eighth-grade students whose teachers received

professional development on computers showed gains in mathematics scores of .35 of a grade

level. Wenglinsky reported that higher order uses of computers and professional development were positively related to students’ academic achievement in mathematics for both fourth- and

eighth-grade students. Schools in which teachers had professional development and used computers to teach higher-order skills also reported lower student absenteeism and higher teacher morale as measured by student and teacher responses on the NAEP (social environment of the school).

The best environments to study mathematics learning and teaching are the classrooms

(Ball & Cohen, 1999). The researchers indicated that mathematics teachers should test their hypotheses and theories in their classrooms, treating the lessons as experiments. Teachers were able to investigate and accumulate hypotheses about their practices, and, when teachers shared investigations with their colleagues, a knowledge base for best practices in mathematics instruction developed. They found that teachers who viewed their classrooms as laboratories for studying the teaching and learning process began collecting information about how students learn and understand conceptual mathematics.

Research has shown that learning is most effective when four fundamental characteristics are present: (1) active engagement, (2) participation in groups, (3) frequent interaction and feedback, and (4) connections to real-world contexts (Bruer, 1993). As applied to mathematics, the pioneers in learning research have also been pioneers in exploring how technologies can improve learning. As scientists have understood more about fundamental characteristics of learning, they have realized that the resources of traditional classrooms often do not provide the

22

support for learning, while technology—when used effectively—can provide ways of teaching

that are better matched to how children learn (Bruer, 1993).

The Benefits and the Challenges

Whether the technology is simple computer-based tutoring programs or more advanced technology solutions suited for student exploration, and whether the goal is to raise student achievement on standardized tests or to serve as a catalyst for school reform, research studies consistently point to specific considerations that support productive outcomes. Those considerations are highlighted and summarized in the research described in the paragraphs that follow.

Although technology can support educational change, it will have little impact without reform at the classroom, school, and district levels. In one example, a study was conducted in

Union City, New Jersey, to explore the impact of a school-business partnership called Project

Explore that was designed to provide students and teachers with access to information resources

(Chang, Henriquez, Honey, Light, Moeller, & Ross, 1998, p. 43). Project students had access at home and at school to a variety of technology tools and non-project students had access only at school and were not provided the resources at home. Researchers examining the impact that this access would have on student achievement found a substantial improvement on students’ standardized test results, particularly at the middle school level, where the scores rose from 30 to

50 percentile points on a state-mandated test. While some of this improvement can be attributed to technology, the researchers found that other efforts that were occurring simultaneously were noteworthy. The multiple treatment interactions included a change in the reading curriculum from skill-based to whole language, the use of authentic literature instead of basal readers, block scheduling, extensive staff development, and increased parent involvement. The positive impact

23

was not due exclusively to the use of technology but rather in the interweaving of a systematic

program of reform with the effective use of technology-based resources (Chang, et al., 1998, p.

43).

Several studies noted that technology had a positive impact on student engagement, but

only under certain conditions, namely when technology was integrated into other aspects of the students’ experiences (Linn, Kessel, Lee, Levenson, Spitulnik, & Slotta, 2000). For example, students were less likely to become bored with computers when teachers used them only as one of many tools employed in the instructional process. Teachers who used computers successfully used them when they were the most appropriate tool for the assignment—not just the tool that was available. Student engagement occurred and was sustained in classrooms where the computer software was not emphasized for drill-and-practice applications and where the classrooms emphasized interdisciplinary, project-based instruction (Sandholtz, et al., 1997).

Porter (1999) supported the benefits of constructing technology-rich learning environments by becoming “accountability ready”. She concluded that school systems must have a shared vision, measurable goals beyond technology for student performance, adequate budgets, effective staff development, stable and robust technical environments, and ongoing assessment as keys to see visible student results. Her research indicated that proving the impact of any technology is complex. The complexity lies in pedagogical practices, beliefs, and values. The results are about actual measurable changes and benefits that occur because of efforts that were made.

As noted earlier in this chapter, studies have indicated that technology will have little effect on school improvement and student learning unless teachers are adequately and appropriately trained. Studies suggested that teachers who receive formal training used

24

technology more frequently for instruction, and this use can lead to significant improvements in student achievement. According to a report by the National Center for Education Statistics

(1999), teachers who indicated they were prepared to teach with technology used it more frequently and in a greater variety of ways. They were more likely to guide their students toward the use of technology tools with tasks that require higher-order thinking. Educational reform efforts should be focused not only on acquiring more technology for the classroom, but also on developing teaching strategies that complement technology use within the curriculum (Pierson,

2001).

In the mathematics content area, it has been argued that teachers must approach the teaching of mathematics differently (Schifter & Riddle, 2004). These practitioners recommended that teachers must be taught how to construct for themselves a more powerful understanding of learning, teaching, and mathematics substance. They suggested that teachers must be challenged at their own level of mathematical competence and confronted with concepts that they have never encountered before. Teachers must envision a new kind of classroom—one that is highly engaging that captures students’ mathematical thinking (Schifter & Riddle, 2004). The National

Council of Teachers of Mathematics (2003) supported this thinking by indicating that technology will provide the type of environment to solve the kinds of problems that students will face in the future.

Specifically, teachers need to be taught how to use technology to deliver instruction.

Helping teachers to learn to integrate technology into curriculum is a crucial factor in the successful implementation of technology in schools (Sivin-Kachala & Bialo, 2000), but most teachers have not had training where technology use was appropriately modeled for them. Even when professional development focuses on technology integration, teachers usually received

25

little follow-up training or support (Statham & Torrell, 1999). Teachers should become familiar with standards and guidelines for the instructional use of technology, such as ISTE’s National

Educational Technology Standards for Teachers (NETS) and the related publication of sample lessons and units, self-assessments, and other resources (International Society for Technology in

Education, 2002).

Researchers investigating the impact of the ACOT teacher development centers found that when teachers were learning to integrate technology into their classrooms, the most important staff-development features included opportunities to explore, reflect, collaborate with peers, work on authentic learning tasks, and engage in hands-on, active learning. The principles for creating successful learning environments for children applied to teachers as well (Sandholtz, et al., 1997).

Without adequate access to technology, even well-trained, highly motivated teachers will not be able to integrate technology effectively into instruction. Although studies are inconclusive about the optimal number of computers per classroom, research is clear that student and teachers are best served if they have convenient, consistent, and frequent access to technology. For example, a RAND study (Glennan & Melmed, 1996) of technology-rich schools suggested that the most successful of these schools had a high ratio of computers per student and high access to them. In these schools, the expenditure per student on technology was three to five times the U.S. average.

While recent surveys about the status of technology in school suggested that the amount of technology is increasing (Market Data Retrieval, 2003), teachers continue to report that lack of access is a barrier to integrating technology. Many schools have obsolete computers. It is not uncommon for schools still to be using computers that are over a decade old (Statham & Torrell,

26

1999). There are wide discrepancies in accessibility from state to state and from school to school,

with high poverty schools typically having fewer technology tools (U. S. Department of

Education, 2003).

Statham and Torell (1999) indicated that a 1:5 computer-to-student ratio would assure

students “near universal access.” In the ACOT project, researchers investigated the impact of

“universal access” by providing all project students with a computer at home and at school.

Throughout the project, researchers learned that it was not important to require a computer on

every desk in order to provide appropriate access. By the end of the ACOT project, most

classrooms had a ratio of about five students per computer.

In addition to the number of computers available, the location of the hardware affected

accessibility (National Center for Education Statistics, 1999; Statham & Torrell, 1999).

Computers can be either in a centralized location (such as a computer lab), distributed (in

classrooms), or some combination of the two. The results from the West Virginia Basic

Skills/Computer Education (BS/CE) program indicated that student achievement is most

improved by the distributed model. Students who had access to computers in their classrooms

showed more improvement in basic skills than those who received instruction in computer labs.

In addition, teachers who had computers in the classroom reported greater confidence and

competence in using computers and more time using the computers (Mann, Shakeshaft, Becker

& Kottkamp, 1999). This study analyzed a representative sample of 950 fifth-grade students’

achievement from 18 elementary schools across the state. These students had been participating

in the programs since 1991. Data were also collected from 290 teachers to show the influence

that the statewide technology initiative had on student achievement. The researchers found that

the more students participated in BS/CE, the more their test scores rose on the Stanford 9. The

27

cost benefit analysis of the study found that the program was more cost effective in improving student achievement than class size reduction from 35 to 20 students, increasing instructional time, or tutoring programs (Mann, et al., 1999).

In addition to evaluating the importance of school access, researchers have investigated the impact of student and teacher use of home computers. In Union City, New Jersey, researchers evaluated a group of project students who had ready access to technology at home and at school.

Their performance on standardized test scores was compared to non-project students who had only limited access at school (Chang et al., 1998). Project students had access to technology tools like word processing, spreadsheet, and database programs, as well as access to communication and research resources like email and the Internet. Non-project students had access to similar resources at school but did not have access to email. Researchers found that the project students performed statistically significantly better than the non-project students on standardized writing tests at the seventh, eighth, and ninth grade levels.

Like students, teachers can often improve their personal skills with access to a home computer. Teachers indicated that they typically do not have the time on the job to learn to use technology, to practice what they learn, or to explore expanded uses of the computer. Teachers who had computers at home indicated that they generally had more time to improve their skills and increase their comfort levels (Chang, et al., 1998).

Many times, technology is purchased without a vision of how it is to be integrated into the mission and goals of the school or district. Research suggested that technology projects should be implemented only after careful planning, where administrators and other stakeholders develop standards and goals for technology use. Since hardware and software are constantly

28

changing, schools and districts must revisit their technology plan on a continual basis and make revisions as necessary to take advantage of new innovations (Sivin-Kachala & Bialo, 2000).

Although adequate access to technology is a key factor in successful implementation, another barrier to technology use is the lack of technical support. Even teachers who enjoy using computers will stop using technology if the equipment is unreliable. Many teachers lack troubleshooting skills or the time to troubleshoot if equipment malfunctions in the middle of a lesson. Therefore, the effective use of technology requires appropriate and adequate school and

district infrastructure and must include timely, on-site technical support.

To use technology effectively, teachers must understand and apply it to the framework of

curriculum standards. In the ACOT study, student engagement remained highest when technology use was integrated into the larger curriculum framework rather than being “added

on” to an already full curriculum. According to Statham and Torrell (1999), however, a survey of

elementary teachers revealed that schools used technology primarily to improve basic skills

rather than integrating it into the curriculum.

Other research has shown the importance of integrating technology into the curricular framework. The West Virginia Basic Skills/Computer Education program integrated technology into instruction rather than isolating computer skills from content learning. Researchers identified this characteristic of the program as one reason for its effectiveness (Mann et al.,

1999).

The Concord Consortium (2003), in collaboration with Harvard University and other education agencies, recently announced a $7 million research study designed to assess the impact of technology on student learning. The five-year project will be a large-scale investigation into the effectiveness of technology applications in improving student learning in the more difficult

29

science concepts. The computer models employed by the group will help students visualize

scientific concepts that cannot be observed in real life. Results from this research will help guide future decisions about how modeling tools should be used in the classroom. Studies like this one will help educational institutions and government agencies to decide how to use technology in education (The Concord Consortium, 2003). The study will involve students in many schools throughout the country and its results are highly anticipated. The important issue in determining effectiveness for learning is not the sophistication level of the technologies, but the ways in which their capabilities assist and motivate the users (Dede, 2002).

As numerous researchers have found, measuring the impact of technology use on student achievement is a difficult task. Classrooms are not laboratories where scientists can impose experimental design. In addition, there are few reliable and valid assessments that measure students’ higher-order thinking skills, problem-solving ability, or capacity to locate, evaluate and use information—skills that many teachers believe can be enhanced through the use of technology. Technology has also been shown to increase student motivation and engagement, prepare students for jobs, and enhance students’ ability to work collaboratively; but few, if any, tools and methods exist that measure impact in these domains. Therefore, it is not surprising that the impact of technology on education continues to be debated.

There is a substantial body of research that suggests that technology can have a positive impact on student achievement under certain conditions and when used for specific purposes.

However, there is no magic formula that educators and policymakers can use to determine if this return is actually worth the investment. According to former Education Secretary Riley (as cited in McNabb, Valdez, Nowakowski, & Hawkes, 1999):

It is the ability of all students—no matter whether rich or poor, or whether they are from a small town, a city, a rural area, or a suburb—to learn at the highest levels with the

30

greatest resources and have the promise of a future of real opportunity. This is the potential of technology (p. 3).

Summary

The suggestions presented in this chapter related to teacher training, access to technology,

and planning should be carefully considered by policymakers and researchers as they seek to

enhance student learning and to improve schools through the use of technology. While studies

point to positive outcomes when technology is integrated throughout the curriculum at the heart

of instruction, it is apparent that certain pre-conditions must exist and be supported before

technology can be maximized to enhance student learning and achievement. School boards,

administrators, and teachers should carefully prioritize their expectations where technology use

is concerned and then commit to a whole-hearted effort to sustain and support its use to achieve

those expectations.

Using technology to improve education is not a simple matter. Vigorous, engaging,

content-rich, and supportive sets of research programs are needed to continue to improve the

mathematics curriculum. To help inform future decisions about ways to improve how and what

students learn, research studies are needed to investigate how programs evolve in the classroom and incorporate the data found in these investigations. Schools desire professional development opportunities that allow teachers to test, revise, and collaboratively implement practices in a culture of professional learning. These challenges will ensure that technology is used effectively

to enhance how and what children learn for the improvement of mathematics instruction and

practice in classrooms. Research indicates that the use of technology as an effective learning tool

is more likely to take place when embedded in a broader education reform movement that

includes improvements in teacher training, curriculum, student assessment, and a school’s

capacity for change. The eMath professional development model was developed to address the

31

need to create engaged learning environments with content growth for all educators and use data to drive instruction in an environment of professional learning communities.

32

CHAPTER 3

DESIGN OF THE STUDY

This chapter reports the design of the study including the methodology, sample selection, instrumentation, procedure for data collection, and analysis of the data. The chapter begins with a review of the research question and a brief explanation of the research process. A detailed description of the collection procedures and the statistical treatment follows.

Research Question

The primary question that guided this study was what impact does the eMath professional development model have on third grade student achievement in mathematics? To investigate this research question, CRCT scores were collected on third grade students whose teachers participated in the year-long professional development model.

Population and Sample

The population used for this study was public elementary schools, third grade, in Central

Georgia, for the school year 2003-2004. Each of these schools was a Title I School and randomly

chosen to participate in the Title II D, Part D, Enhancing Education Through Technology,

through a competitive grant award as designed by the Georgia Department of Education. The

grant process and subsequent award required that 25% of the grant funds be spent for

professional development. Two school districts served by the Educational Technology Training

Center at Macon State College received funding for the grant during 2003-2004 school year. The

eMath project was selected by these districts as the professional development model for four

33

elementary schools in these school districts, with 12 teachers participating. A total of 232

students were taught by these teachers.

Each teacher received 30 hours of professional development from an eMath instructor

throughout the school year. The techniques and strategies taught were implemented in the

classroom with the students over the course of the 2003-04 academic year. In April of 2004, the

Criterion-Referenced Competency Test (CRCT) was administered to the third grade students,

and the data were collected. The CRCT is designed to measure how well students acquire the

skills and knowledge described in the Quality Core Curriculum (QCC). The assessments yield

information on academic achievement at the student, class, school, system, and state levels. This

information is used to diagnose individual student strengths and weaknesses as related to the

instruction of the QCC, and to gauge the quality of education throughout Georgia (Georgia

Department of Education, 2004).

The experimental group was composed of four elementary schools—two from each

district in the study. The experimental group consisted of 232 students. The control group was

composed of three schools—two from one district and one from the other. The control group

consisted of 218 students. The first school district, with a population of over 150,000, is part of

the Macon Metropolitan Statistical Area (MSA). The second district, with a population of over

58,000, is part of the Atlanta Metropolitan Statistical Area (MSA). All seven schools are Title I

schools. Table 3.1 provides demographic data from both the experimental schools and the

control schools.

34

Instrumentation

The CRCT mathematics raw scores from the spring of 2003 were used to determine the

student performance in meeting the QCC standards in third grade mathematics. All subtotals and

totals were used for analysis.

Table 3.1

Demographic Data for Experimental and Control Schools

School % Student Population % Eligible No. Type Total Enrollment in Special Education Free/Reduced Meals

1 Experimental 441 16.8 95

2 Experimental 532 17.1 96

3 Experimental 334 11.4 90

7 Experimental 626 17.9 67

Experimental Mean 15.8 87

4 Control 389 6.9 62

5 Control 497 13.7 96

6 Control 686 13.8 89

Control Mean 14.8 82

As an indicator of reliability, Cronbach’s alpha coefficient is presented in the table below

by grade and content area for the 2002 operational CRCT. Cronbach’s alpha is a measure of

internal consistency (reliability), which indicates how well all the items in the test measure one single underlying ability. The alpha value represents the estimated average correlation between all possible split combinations of the test. Table 3.2 provides grade level, content area, and alpha

35

values for each content area included in the CRCT (Shirley Millicans, Georgia DOE Testing

Division, personal communication, April 25, 2005).

The analysis of the data was completed according to the research questions. These data provided descriptive statistics that answered the questions regarding the third grade mathematics achievement among the experimental group and the control group.

Research Design

The research design included a posttest-only control group design (Campbell & Stanley,

1963). The experimental group consisted of 232 third grade students whose teachers were taught by an eMath trained teacher. These classes were located in Title I elementary schools that were randomly chosen by the GaDOE. The control group consisted of 218 third grade students whose teachers were not taught by an eMath trained teacher. These classes were located in comparable

Title I elementary schools that were randomly chosen by the GaDOE. The CRCT was administered to all third grade students in the spring of 2003 in all elementary schools in

Georgia.

The eMath professional development model was designed to be implemented during the course of an academic year. Teachers met face-to-face with an eMath trainer for five days (30 hours of training) at the training center and were observed in their classrooms on at least three occasions. During the face-to-face training, the teachers learned best practices for administering the STAR Math™ assessment to evaluate student needs in meeting the mathematics curriculum objectives. This diagnostic assessment was administered three times during the year. The teachers received instruction on classroom management strategies in incorporating Accelerated

Math™ into their daily mathematics instruction. Specifically designed classroom

36

Table 3.2

Georgia Criterion Referenced Competency Test Alpha Values

Grade Content Area alpha 1 Reading 0.88 1 ELA 0.89 1 Mathematics 0.91 2 Reading 0.86 2 ELA 0.89 2 Mathematics 0.92 3 Reading 0.91 3 ELA 0.91 3 Mathematics 0.94 3 Science 0.91 3 Social Studies 0.93 4 Reading 0.90 4 ELA 0.90 4 Mathematics 0.93 4 Science 0.90 4 Social Studies 0.91 5 Reading 0.89 5 ELA 0.91 5 Mathematics 0.93 5 Science 0.90 5 Social Studies 0.92 6 Reading 0.90 6 ELA 0.92 6 Mathematics 0.94 6 Science 0.91 6 Social Studies 0.91 7 Reading 0.89 7 ELA 0.91 7 Mathematics 0.92 7 Science 0.90 7 Social Studies 0.89 8 Reading 0.90 8 ELA 0.92 8 Mathematics 0.94 8 Science 0.90 8 Social Studies 0.88

Source: Georgia Department of Education (Shirley Millicans, Georgia DOE Testing Division, personal communication, April 25, 2005).

37

management kits were provided to the teachers that were to be used in their classrooms as part of

the implementation of the project. The teachers were taught specific mathematics instructional

strategies designed to target those students who continued to struggle in meeting the curriculum

objectives. During the classroom observations, trainers used observation rubrics to measure the

level of implementation of the software and the instructional practices at the classroom level.

Statistical Treatment

The independent variable in this study was participation or non-participation in the eMath

professional development model. The unit of analysis was individual student scores of those

teachers who participated in the training. A one-way Analysis of Variance was used to test the

null hypothesis that there was no statistically significant difference on mean CRCT scores among

third grade students whose teachers were trained or not trained in eMath. The dependent

variables in this study were CRCT mathematics scores for third grade students (totals and

subtotals).

Summary

This chapter has described the procedures and methods employed to analyze the data in

this study. The description included information on the instrumentation, details about the

subjects in the sample population, and the statistical treatment used in analyzing the data. This

information will be used in an effort to answer the research question, what impact does the

eMath professional development program have on third grade student achievement in mathematics?

38

CHAPTER 4

PRESENTATION AND ANALYSIS OF DATA

Data Collection

Criterion Reference Competency Test (CRCT) scores were collected from each of the

schools. The superintendents in each of the school systems were contacted and gave permission

for the data to be used for the purposes of this research project. The principals at each of the

schools were then contacted to secure the data (without identifying names or numbers).

Additionally, information about class size, ethnicity, and student socioeconomic status were collected from the Georgia School Report Card obtained from the Georgia Department of

Education web site. This information was used to minimize bias in the findings.

These data were entered into a spreadsheet, and organized with the following columns for identification: school number, teacher number, school type (experimental or control), birth month, birth year, gender, mathematics total, number sense and numeration, geometry and measurement, patterns and relationship/algebra, statistics and probability, computation and estimation, and problem solving. The schools, teachers, and students were anonymous. These procedures and methods of data collection were used to answer the following research question:

Did students whose teachers participated in the eMath professional development model perform statistically significantly better on standardized tests than students whose teachers did not participate in the eMath professional development model?

39

Student Achievement

Since the focus of this study centered on student achievement in mathematics, the

collected data were reviewed carefully to analyze differences in mathematics achievement

between the experimental and control groups. The researcher analyzed all the student mathematics totals and subtotals scores [n=232 (experimental group) and n=218 (control group)]. Using SPSS Software Version 13, a one-way ANOVA was used to analyze scores from

both groups. The one-way ANOVA compares the means of one or more groups based on one

independent variable. The researcher examined the variation within the groups, and then tested

how that variation would translate into mean differences between the groups, considering the

number of subjects in the groups. In this study, there were only two groups, so variation between

groups was the difference between the means.

The control group was compared to the experimental group and found to be statistically

significantly higher than the experimental group. Table 4.1 summarizes these mean scores.

It was determined that there were statistically significant differences on six of seven

categories as noted in Table 4.2. In all six cases, the control group’s mean was significantly

greater than the mean of the experimental group (p<.05). There was no statistically significant

difference in the patterns and relationships/algebra subscore (F=.451, p=.502).

Based on the data presented in Table 4.3 and Table 4.4, the demographic characteristics

of each school were carefully reviewed and analyzed. These tables provide more detailed

information regarding the specific third grade sample from each school, including student percentages based on race, gender, socio-economic status, and age. The ethnicity percentages and free/reduced lunch eligibility data were analyzed, and it was determined that Schools 6

(control) and 7 (experimental) were not comparable with the other groups in the free/reduced

40

lunch eligibility levels. Because these two schools had a higher socio-economic status than the

others (less than 70% as compared to over 90%), the data from these two schools were extracted and evaluated separately. Thess data were analyzed separately by using a one-way ANOVA, and it was determined that there was no statistically significant difference between the mean scores

on six out of seven categories of these two schools. The only statistically significant difference was found in the comparison of the means of the computation and estimation subtotal (F=5.531,

p=.020). Table 4.5 summarizes these findings.

Table 4.1

Descriptive Statistics of Groups (ne=232, nc=218)

Std. Total and Subtotals Type Mean Deviation

Math Total Experimental 319.37 24.114 Control 325.56 20.614

Number Sense and Experimental 321.06 27.520 Numeration Control 326.77 24.670

Geometry and Experimental 321.96 26.962 Measurement Control 326.87 24.778

Patterns and Relationship/ Experimental 321.16 31.138 Algebra Control 323.12 31.032

Statistics and Probability Experimental 318.97 35.281 Control 326.87 30.276

Computation and Experimental 319.94 30.757 Estimation Control 328.94 28.752

Problem Solving Experimental 320.31 32.210 Control 327.31 29.558

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Table 4.2

Differences Between Groups in Seven Mathematics Subscores (N=450)

Total and Subtotals Sum of Squares df Mean Square F Sig.

Math Total Between Groups 4310.917 1 4310.917 8.525 .004 Within Groups 226533.6 448 505.655 Total 230844.5 449

Number Between Groups 3563.381 1 3653.381 5.331 .021 Sense and Within Groups 307013.1 448 685.297 Numeration Total 310666.5 449

Geometry Between Groups 2709.620 1 2709.620 4.031 .045 and Within Groups 301150.7 448 672.211 Measurement Total 303860.3 449

Patterns and Between Groups 435.595 1 435.595 .451 .502 Relationship/ Within Groups 432938.1 448 966.380 Algebra Total 433373.7 449

Statistics and Between Groups 7001.611 1 7001.611 6.448 .011 Probability Within Groups 486453.0 448 1085.833 Total 493454.6 449

Computation Between Groups 9096.401 1 9096.401 10.242 .001 and Within Groups 397902.5 448 888.175 Estimation Total 406998.9 449

Problem Between Groups 5502.437 1 5502.437 5.743 .017 Solving Within Groups 429254.1 448 958.156 Total 434756.5 449

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Table 4.3

Student Demographic Information (N=450)

School Type No. of 3rd % Eligible Race-Ethnicity Sex No. Graders Tested Free/Reduced % % Meals % Black % White Female Male 1 Exp. 65 97% 1% 65% 35% 95%

2 Exp. 62 94% 5% 42% 58% 96%

3 Exp. 40 91% 6% 47% 53% 90%

7 Exp. 90 47% 43% 51% 49% 67%

6 Ctrl. 105 47% 47% 44% 56% 62%

4 Ctrl. 44 99% 1% 43% 57% 96%

5 Ctrl. 70 89% 8% 59% 41% 89%

Table 4.4

Student Distribution by Age

School No. Age 9 Age 10 Age 11 Age 12 1 35% 54% 10% 1%

2 42% 23% 4% 6%

3 28% 58% 14% 0%

4 36% 49% 13% 2%

5 54% 42% 2% 2%

6 42% 52% 7% 0%

7 42% 53% 5% 0%

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Table 4.5

Differences Between Schools 6 and 7

Total and Subtotals Sum of Squares df Mean Square F Sig.

Math Total Between Groups 590.061 1 590.061 1.169 .281 Within Groups 95427.39 189 504.907 Total 96017.46 190

Number Between Groups 881.253 1 881.253 1.320 .252 Sense and Within Groups 126176.7 189 667.602 Numeration Total 127058.0 190

Geometry Between Groups 93.857 1 93.857 .155 .694 and Within Groups 114562.0 189 606.148 Measurement Total 114655.9 190

Patterns and Between Groups 145.191 1 145.191 .146 .702 Relationship/ Within Groups 187395.4 189 991.510 Algebra Total 187540.6 190

Statistics and Between Groups 216.113 1 216.113 .222 .638 Probability Within Groups 184102.6 189 974.088 Total 184308.7 190

Computation Between Groups 4834.118 1 4834.118 5.531 .020 and Within Groups 165199.7 189 874.073 Estimation Total 170033.8 190

Problem Between Groups 89.118 1 89.118 .189 .765 Solving Within Groups 188750.3 189 998.679 Total 188839.5 190

In order to determine if there were pre-existing differences between the groups in seven

subscores, age was used as a covariate. The relationship between age and each of the seven

subgroups in mathematics was examined. It was found that although there was a statistically

significant relationship between the math total and age at the .01 level, it was not a practical

relationship (R squared=.07). Table 4.6 summarizes this relationship.

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An analysis was done to compare achievement score means between genders. At the p=.10 level, it was determined that there was a statistically significant interaction between gender and treatment. It was found that males at the experimental schools did not perform as well as the males at the control schools. Table 4.8 reflects these findings. In Table 4.9, descriptive statistics are presented that summarize the mean scores among genders. It was found that there was no statistically significant difference between genders. And within each group (experimental and control) there was no gender difference in achievement scores.

Table 4.6

Tests of Between-Subjects Effects (Mathematics Total)

Source Type III Sum of df F Sig. Squares

Type 324.475 1 .685 .408

Age 12956.375 1 27.353 .000

Type * Age 471.558 1 .996 .319

Error 205098.507 433

Total 45750981.0 437

Corrected Total 222494.023 436

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Table 4.7

Demographic Information for Gender and Age (All Students) (n=245)

School No. Female Male Age 9 Age 10 Age 11 Age 12

1 67% 33% 36% 54% 8% 2%

2 43% 57% 43% 47% 5% 5%

3 51% 49% 27% 61% 12% 0%

4 43% 57% 36% 49% 13% 2%

5 56% 44% 57% 39% 2% 2%

Table 4.8

Tests of Between-Subjects Effects (Mathematics Total)

Source Type III Sum of Df F Sig. Squares

Type 4181.018 1 8.310 .004

Gender 715.684 1 1.422 .234

Type * Gender 1474.724 1 2.931 .088

Error 224404.547 446

Total 45994965.000 450

Corrected Total 230844.500 449

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Table 4.9

Descriptive Statistics for Gender (Mathematics Total)

Type Gender Mean Std. Deviation N

Experimental Female 319.89 21.156 121 Male 318.79 27.059 111 Total 319.37 24.114 232

Control Female 322.37 21.104 105 Male 328.52 19.783 113 Total 325.56 20.614 218

Total Female 321.04 21.121 226 Male 323.70 24.114 224 Total 322.37 22.674 450

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CHAPTER 5

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

The purpose of this study was to assess the eMath model’s impact on third grade mathematics achievement scores on the Criterion-Referenced Competency Test (CRCT). The following research question was examined: Did students whose teachers participated in the eMath professional development model perform statistically significantly better on standardized tests than students whose teachers did not participate in the eMath professional development model?

Chapter 1 of this study described the problem, purpose, and importance of this study. The review of related literature in Chapter 2 detailed the examination of previous research in the area of technology integration at the heart of instruction in the content areas. Chapter 3 described the collection of data and the procedures that were used to analyze the data. In Chapter 4, data were analyzed and presented in a multitude of charts, graphs, and tables. The final chapter, Chapter 5, is intended to summarize the findings from this study and suggest recommendations for further study in the area of professional development for the integration of technology into the classroom and curriculum content area.

The professional development model was delivered throughout the 2003-2004 school year. The implementation of the model for this first year began in August and ended in May.

During this 10-month period, teachers participated in experiences that were designed to enhance their use of technology in the mathematics classroom. They implemented identified best practices for using STAR Math™ and Accelerated Math™ to enhance their mathematics

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instruction. The expectations for change were defined, and the timeline for implementation was

described.

Conclusions

This study answered the research question: Did students whose teachers participated in

the eMath professional development model (for a 10-month period) perform statistically

significantly better on standardized tests than students whose teachers did not participate in the

eMath professional development model? The research has concluded that students whose

teachers participated in the eMath professional development model for only 30 hours did not

score statistically significantly higher on the mathematics portion of the CRCT. In fact, students

whose teachers employed traditional instructional practices in the control schools performed

statistically significant better than those students in the experimental schools.

Gender was found to have no significant relationship in the mathematics scores on the

CRCT. Since the schools were similar in their free/reduced eligibility percentages, socio-

economic status was not a factor in the test scores.

Lessons Learned and Recommendations

The Trends in International Mathematics and Science Study (TIMSS, formerly known as the Third International Mathematics and Science Study) established that American teachers tend to adopt surface features of the NCTM Standards documents without changing their teaching beliefs (Stigler, 1977, p. 15). This research project’s findings were consistent with Stigler’s.

Coaching alone is not enough due to the limited transfer of knowledge. The eMath trainers reported that teachers tended to look for “quick fixes” without making improvements in their instructional practices. The Japanese model of lesson study mentioned earlier in the review of

literature has the potential for increasing engagement among teachers to improve the quality of

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their instruction (Richardson, 2004). With the implementation of new mathematics performance standards in Georgia, instruction in Georgia’s schools will be forced to undergo significant change. As future instances of eMath are delivered to teachers, these recommended concepts should be given careful consideration in order to transform classrooms into highly engaged, performance-based environments.

In the study by Holmes and Brown (2003), the use of Accelerated Math™ for targeting student computation skills in mathematics was found to have significantly improved student performance on the STAR Math™ diagnostic assessment. The researchers reported that program support to teachers from administrators was rated very highly among all of the teachers at the experimental schools. Anecdotal data collected by the eMath trainers indicated that future training with this model should be adjusted to attend to consistent and ongoing interaction with school administrators. Administrative support was reported by the trainers to be lacking in this pilot implementation.

When planning content instruction for teachers, it is critical to design the goals and objectives based on student needs rather than teacher needs. The process of lesson study begins with the question, “how can we help learners?” The focus should be on the students and their learning needs a vehicle for teacher growth (Richardson, 2004). For future professional development with the eMath model, there should be more opportunities for participation, ownership, individualization, and accountability. The analysis and interpretation of student data should be emphasized at the classroom level. Multiple student assessment data and their analysis were not attended to in this pilot implementation of eMath.

eMath trainers reported that teachers consistently were pulled from training sessions to attend to other school-related matters. Future research should be cognizant of external and

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internal influences in the school. The impact on teachers should be examined as district and

school plans are implemented.

In a study conducted with 659 teachers, Rakes and Casey (2002) found that teacher

concerns and perceptions about the integration of technology into the curriculum had significant

influence on successful implementation of technology innovations with curriculum. They

reported that the intense, personal concerns of teachers may have been sacrificed as more

emphasis was placed on student achievement. They summarized that if it is desirable for teachers

to be concerned with the application and use of technology with and for students, teachers' personal concerns must be addressed first. Their findings indicated that teacher concerns about innovations appeared to be developmental in that earlier concerns must first be resolved (lowered in intensity) before later concerns emerge (increase in intensity). If the early concerns toward technology remained intense, they found that teachers attempted to discontinue its use, in order to reduce the intensity of these concerns. In general, however, they reported that a person's concerns about an innovation developed toward the later stages (i.e., toward impact concerns) with time, successful experience, and the acquisition of new knowledge and skill. The current links between technology in education and constructivist learning environments will succeed more favorably if teachers’ beliefs are considered and confronted. Otherwise, despite the quantity of resources poured in the purchase of hardware and software in schools may result in a waste of energy and resources. Certainly, a more grass-root perspective adoption is needed in future implementation approaches. Based on conversations that the researcher had with the technology staff at the school and district levels, teachers’ beliefs were not considered when providing them with basic technology integration training. The teachers believed that classroom- level technology support was not adequate to address their needs.

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Lambert (2002) recognized the need to develop leadership capacity among all the members of the school community as a meaningful step toward transforming the culture of the school. She reported that the traditional model of one-person leadership leaves the substantial talents of teachers virtually untapped. Improvements achieved under this traditional model are not easily sustained; the hope and promise often leaves with the principal. She indicated that instructional leadership must be a shared, community responsibility and is the professional work of everyone in the school. The eMath pilot program was unable to address teacher leadership among the participating teachers.

According to Hiebert (2001), in order to gain long-term results in improving instructional practice, the focus must be on continuous, steady improvement rather than “quick fixes.” For improvement to be lasting, he indicated that student learning is improved through measurable goals and lessons learned from the implementation process. The researcher will use this experience and research to modify the program to target the school’s culture in order to get long- terms results for future implementation. Scherer (2000) found that reforms that came from within the school community were the most supported by those who believed in them. In order for the reforms to live on after the originators’ tenure, change leaders assisted in capturing the public imagination and created a broad social movement. These eMath schools sought to use specific software for addressing content rather than focusing on instructional practice. Future implementation of this model must address student performance and understanding at the core of instruction.

Once systemic change is under way, people often feel overwhelmed by the magnitude of the work. Fullan (2001) referred to the “implementation dip” as that period of time early in a change implementation where, no matter how much everyone plans for change, hurdles occur.

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He indicated that it is during these challenging times of early implementation when the culture of

community—of shared vision—will reap its rewards. Building a collaborative community of

leadership takes time, so a change leader should encourage others to forge ahead and stay true to

the course they designed together. This model did not provide an adequate amount of time or

guidance in assisting teachers in developing or maintaining a culture of professional community

among the math teachers. Reculturing—changing what people believe and what value they place

in the organization—is key to sustained change (Eaker, DuFour, & DuFour, 2002; Fullan, 2001;

Glickman, 2002). Transforming a school’s culture into a culture of change means producing the

capacity to seek, critically assess, and selectively incorporate new ideas and practices.

Reculturing involves hard, labor-intensive work (Eaker, et al., 2002). It is a work, however, that

should be addressed in any future recurrences of this professional development model.

The eMath model in its pilot year did not provide ample opportunity for teacher collaboration with administrators. Having the best ideas is not as critical as helping others assess and find meaning and commitment to new processes and strategies through collaboration and inquiry. Leaders must guide the organization into a culture of community—a community that fosters professional relationships that serve to inspire others to think differently (Fullan, 2001).

Change leaders who motivate and energize teachers to build a foundation of trust and collaboration will help to engage those disconnected teachers and create a resource that will continue to grow. As the eMath model is modified and implemented in the future, careful attention must be given to administrative and instructional collaboration.

This pilot study of the eMath professional development model has provided many opportunities for future research consideration. Many revisions are currently underway to address the research that supports effective professional development. Future implementation of

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this project has and will continue to reflect on best practices, current research, and ongoing, seamless feedback and assessment.

Based on the synthesis of research presented in this study, specific recommendations for future implementation of the eMath model should include:

• Using multiple assessment data to analyze student performance to inform instruction.

Clearly with the implementation of the Georgia Performance Standards in Georgia,

training should focus on supporting teachers in the design of curriculum that is

aligned with the mathematics performance standards. Instruction should be carefully

aligned to the needs of students as articulated through ongoing and consistent

feedback. Continuous, ongoing improvement will be the result as teachers focus on

long-term results with improved practice.

• Securing a commitment from administrators to fully support the eMath

implementation. It is recommended that administrators become totally immersed in

the training, classroom implementation, collaboration, and assessment of the

program. Administrative support is crucial to the success of future occurrences of this

program.

• Using technology tools in the context of meaningful and worthwhile learning

experiences. Training with technology tools (i.e., interactive white boards,

calculators, computers, etc) should support and enhance the instructional practices

and strategies of classroom teachers. It is critical that teachers have numerous

opportunities to practice, develop, refine, and implement instructional strategies that

make appropriate use of 21st Century technology tools.

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• Reculturing the school into a professional learning community. Careful attention

should be given to supporting the teachers and the school as they grow into a

community of practitioners who share a common vision and trust. Through guided,

face-to-face collaborative sessions and online opportunities for reflection and

discussion, future eMath delivery should have professional learning communities of

practice at its center. The development of teacher leaders should be a cornerstone of

successful implementation.

Implications for future research exist as a result of this study. This study was conducted in Central Georgia. As the program is refined, repeated studies in the same region as well as other regions in Georgia could prove to be beneficial. As mentioned earlier, with the implementation of the Georgia Performance Standards in mathematics, coupled with the eMath revisions that better align with those standards, a repeated study could provide insight into student understanding in mathematics. Additional studies should consider determining if there were pre-existing differences between the groups by using a covariate at the beginning.

The research is clear when identifying successful professional development and the essential conditions that must exist in order to realize improved student achievement. As similar models of professional learning are implemented, it is highly recommended that research inform implementation practices. The research presented in this study could be examined as an effective starting place for the design and execution of future models of professional learning in elementary mathematics. Perhaps the research question, “How much training will produce a statically significant difference in achievement scores?” should be a focus of future research endeavors.

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