The Computing Essentials
Emprical Study on Multiple Intelligence Differentiated Instructional Material
Declan Kelly 1, Brendan Tangney 2
1 National College of Ireland, Dublin, Ireland
2 University of Dublin, Trinity College, Ireland
Abstract: Research on learning has shown that students learn differently and that they process knowledge in various ways. One such learning theory is Gardner’s Multiple Intelligences which states that different intelligences are used to solve problems and fashion products. EDUCE is an Intelligent Tutoring System that utilises learning characteristics and the Multiple Intelligence concept to provide an individualised learning environment. This paper investigates the learner’s response when faced with a choice of Multiple Intelligence differentiated instructional material. The results from this study suggest that learners do exhibit preferences for different Multiple Intelligence differentiated material and within a group of learners there is a diversity of preferences.
Keywords: Student Modeling, Learning Styles, Pedagogical Framework, Instructional Design, Multiple Intelligences
1 Introduction
Research on learning has shown that students learn differently, that they process and represent knowledge in different ways, that it is possible to diagnose learning style and that some students learn more effectively when taught with preferred methods [1]. However, observing and identifying learning characteristics is difficult and traditionally questionnaires and psychometric tests are used to assess and diagnose learning characteristics. Several systems adapting to learning characteristics have been developed [2, 3]. Typically these systems contain a variety of instructional types such as explanations, or fragments of different media types representing the same content, with the tutoring system choosing one based on the model it has of the student. However it is not clear which aspects of learning characteristics are worth modelling, how the modelling can take place and what can be done differently for users with different learning styles [4].
EDUCE[5] is an Intelligent Tutoring System that aims to utilise learning characteristics to provide an individualised learning environment. It uses a pedagogical model based on Gardner’s Multiple Intelligence concept [6] to classify content, model the student and deliver material in diverse ways. The multiple intelligence concept defines intelligence as the capacity to solve problems or fashion products that are of value and states that there are eight different ways to demonstrate this intelligence. It is a concept that offers a framework and a language for developing a broad range of content that supports creative, multimodal teaching. In EDUCE four different intelligences are used: verbal/linguistic, visual/spatial, logical/mathematical and musical/rhythmic intelligences. Currently, science is the subject area for which content has been developed. Full details on the EDUCE framework for using the Multiple Intelligence concept can be found in [7].
In the construction of an Intelligent Tutoring System such as EDUCE some key issues need to be addressed. There is the need to develop a model for using Multiple Intelligences and to develop content that reflects the principles of Multiple Intelligences. There is the need to investigate the learner’s choice when faced with a selection of Multiple Intelligence differentiated instructional material, to identify are there consistent patterns or is it just a random selection. There is the need to develop adaptive technologies that update the student model and allow for flexible delivery in presentation. There is also the need to analyse the learning gain when using an Intelligent Tutoring System with Multiple Intelligence inspired material.
This paper describes an investigation into learner’s response when faced with a choice of Multiple Intelligence differentiated instructional material. It describes an experimental study that was carried out with two student groups of different backgrounds and ages. During the course of the study students could navigate through the tutorial and make choices about which instructional strategy to use. By automatically observing the student’s interaction, using questionnaires and video, an analysis of individual learning characteristics could be made.
The results from this study suggest a number of points. It suggests that the majority of learners do exhibit preferences for different Multiple Intelligence differentiated instructional material. It suggests that within a group of learners there is a diversity of preferences. It also suggests that EDUCE’s flexibility can facilitate learning gain and that it is a useful tool for investigating learning characteristics.
2. Experimental Study
The experimental study was conducted with two groups of students with different backgrounds. Group A consisted of 11 female students aged 12 to 14 and Group B consisted of 14 female students age 14 to 16. During the study each student navigated through a tutorial on Static Electricity making choices about which Multiple Intelligence differentiated material to view. During the tutorial session, the student’s choice of verbal/linguistic (VL), visual/spatial (VS), logical/mathematical (LM) and musical/rhythmic intelligences (MR) were recorded automatically. Qualitative feedback was received by getting students to fill out questionnaires and by having a general feedback session. Two features of the interaction summaries were used to explore the learning characteristic of the student
· The choice made on first viewing a new learning unit.
· The total choices for each category during the entire interaction. This includes choices made when navigating back through the material
Full details of individual student preferences can be found in [8].
To demonstrate the diversity of preferences among the two groups of students, two measures where used to calculate the student’s preference relative to other students in the class. The first involved calculating how much each type of material was used and comparing it to the average for the class. The units of measurement are the standard deviation from the average. A student was deemed to have a strong preference for a particular type of material if there use of that material was one standard deviation about the average. The second invovled calculating how did their use of a particular type of material rank in comparison to the rest of the class. A student was deemed to have a strong preference for a particular type of material if they were one of the top four users of a particular type of material. Table 1 and 2 show the results for group A and B
Table 1: Group A Results Table 2 Group B Results
The results suggest that within a given group of students, there is range of preferences with different students having different strengths or preference. To demonstrate this in quantitative terms, Tables 3 and 4 give the averages for the different categories. These results suggest that each intelligence is attractive to different groups of students. Some macro-trends to note are the preference for musical/rhythmic intelligence and the dislike for logical/mathematical intelligence. The results suggest that even though the subject content is Science, teaching it using musical/rhythmic intelligence will appeal to a large number of students. Logical/mathematical intelligence is the least attractive for the students and in particular cases some students did not even bother to try it. It may be of note that the sample group for this study was all female. Future studies will be undertaken with male students to explore this further.
VL / LM / VS / MR25 / 9 / 33 / 33
/ VL / LM / VS / MR
26 / 17 / 24 / 34
Table 3. Group A Averages / Table 4. Group B Averages
The results presented here suggest that the majority of learners do exhibit preferences for different Multiple Intelligence differentiated instructional material. It suggests that within a group of learners there is a diversity of preferences. It also suggests that EDUCE can facilitate learning gain and that it is a useful tool for investigating learning characteristics. In the future, further empirical work and more tightly focused parameters are planned in pursuit of a greater understanding in how Multiple Intelligence differentiated instructional material can contribute to the student’s learning experience
References
[1] Riding, R. & Rayner. S, (1997): Cognitive Styles and learning strategies. David Fulton Publishers.
[2] Gilbert, J. E. & Han, C. Y. (1999): Arthur: Adapting Instruction to Accommodate Learning Style. In: Proceedings of WebNet’99, World Conference of the WWW and Internet, Honolulu, HI.
[3] Milne, S. (1997): Adapting to Learner Attributes, experiments using an adaptive tutoring system. Educational Pschology Vol 17 Nos 1 and 2, 1997
[4] Brusilovsky, P. (2001): Adpative Hypermedia. User Modeling and User-Adapted Instruction, Volume 11, Nos 1-2. Kluwer Academic Publishers.
[5] Kelly, D. & Tangney, B. (2002): Incorporating Learning Characteristics into an Intelligent Tutor. In: Proceedings of the Sixth International on ITSs, ITS2002.
[6] Gardner H. (1983) Frames of Mind: The theory of multiple intelligences. New York. Basic Books.
[7] Kelly, D. (2003). A Framework for using Multiple Intelligences in an ITS. To be published In: Proceedings of EDMedia’03, World Conference on Educational Multimedia, Hypermedia & Telecommunications, Honolulu, HI.
[8] Kelly, D. (2002). Technical Report: crite-tr-2002-12-1 @ www.cs.tcd.ie/crite/pubs.htm.