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«On the Dynamic Multiple Intelligence Informed Personalization of the Learning Environment A thesis submitted to the University of Dublin, Trinity ...»

-- [ Page 1 ] --

On the Dynamic Multiple Intelligence Informed

Personalization of the Learning Environment

A thesis submitted to the

University of Dublin, Trinity College

for the degree of

Doctor of Philosophy

Declan Kelly

Department of Computer Science

University of Dublin,

Trinity College,

December, 2005

i

Declaration

The work presented in this thesis is, except where otherwise stated, entirely that of the

author and has not been submitted as an exercise for a degree at this or any other

university.

Signed:

_________________________

Declan Kelly December, 2005 ii Permission to Lend or Copy I agree that the library of the University of Dublin, Trinity College of Dublin, has my permission to lend or copy this thesis.

Signed:

_________________________

Declan Kelly December, 2005 iii Acknowledgments Firstly, I would like to thank my supervisor, Brendan Tangney, for all his help, support and guidance throughout my academic endeavours.

Secondly, I would like to thank all the members of the CRITE research group, who provided many hours of discussion and were instrumental in the development of the ideas presented in this thesis. In particular, I would like to mention Ann Fitzgibbon and Inmaculada Arnedillo-Sánchez who provided crucial comments at critical times in the Ph.D. research journey.

Thirdly, I would like to express gratitude to Stephan Weibelzahl and Peter Brursilovsky who provided insightful comments on different papers submitted during the course of the research and on early drafts of the thesis. In addition, I would like to express thanks to the many anonymous reviewers of papers I have submitted to conferences and to the many different people I have met at conferences who have provided extremely useful comments.

I would also like express gratitude to all the teachers and students who participated in the different studies including the students from John Scottus School, St. Dominics (Cabra), St. Benildus College (Stillorgan) and all the participants in Discovering University (National Colllege of Ireland, 2004).

Above all, I would like to thank my parents who have been incredibly supportive throughout my academic career. Thank you so much.

iv Abstract Educational research informs us “one size does not fit all” (Reigeluth, 1996). It states that learners, reflecting individual traits, possess different learning characteristics, process and represent knowledge in different ways, prefer to use different type of resources and exhibit consistent observable patterns of behaviour (Riding & Rayner, 1998). Research also suggests that it is possible to diagnose a student’s learning traits and that some students learn more effectively when instruction is adapted to the way they learn (Rasmussen, 1998).

Within the field of technology enhanced learning, adaptive educational systems offer an advanced form of learning environment that attempts to meet the needs of different students (Brusilovsky, 2003). Such systems capture and represent, for each student, various characteristics such as knowledge and traits in an individual learner model.

Subsequently, using the resulting model it dynamically adapts the learning environment for each student in a manner that attempts to best support learning.

However, there are many unresolved issues in building adaptive educational systems that adapt to individual traits. Major research questions still outstanding include: what is the appropriate educational theory with which to model individual traits, how are the relevant learning characteristics identified and in what way should the learning environment change for users with different learning characteristics (Brusilovksy, 2001)?

This thesis describes how the adaptive intelligent educational system, EDUCE, addresses these challenges and demonstrates how dynamic adaptive presentation of content can improve learning.

Firstly, EDUCE uses Gardner’s theory of Multiple Intelligences (MI) as the basis for modelling learning characteristics and for developing different Multiple Intelligence informed versions of the same instructional material (Gardner, 1983). The theory of Mutliple Intelligences reflects an effort to rethink the theory of measurable intelligence embodied in intelligence testing and suggests that they are eight different intelligences that are used to solve problems and fashion products.

The thesis also describes how EDUCE’s novel predictive engine dynamically identifies the learner’s Multiple Intelligence profile from interaction with the system and makes predictions on what Multiple Intelligence informed resource the learner prefers.

Based on data coming from the learner’s interaction with the system, the predictive engine uses a novel set of navigational and temporal features that act as behavioural v indicators of the student’s learning characteristics. Empirical studies conducted validated the performance of the predictive engine.

Empirical studies were also conducted to explore how the learning environment should change for users with different characteristics. In particular it explored: 1) the effect of using different adaptive presentation strategies in contrast to giving the learner complete control over the learning environment and 2) the impact on learning performance when material is matched and mismatched with learning preferences.





Results suggest that teaching strategies can improve learning performance by promoting a broader range of thinking and encouraging students to transcend habitual preferences. In particular, they suggest that students with low levels of learning activity have most to benefit from adaptive presentation strategies and that surprisingly learning gain increases when they are provided with resources not normally preferred.

In summary, the main contributions of this research are:

• The development of an original framework for using Multiple Intelligences to model learning characteristics and develop educational resources.

• A novel predictive engine that dynamically determines a learner’s preference for different MI resources.

• Results from empirical studies that support the effectiveness of adaptive presentation strategies for learners that display low levels of learning activity.

vi Related Publications Kelly, D., Durnin, S., & Tangney, B. (2005a). ‘First Aid for You’: Getting to know your Learning Style using Machine Learning. Paper presented at the Fifth IEEE International Conference on Advanced Learning Technologies, ICALT'05, Kaohsiung, Taiwan, 1-4.

Kelly, D., & Tangney, B. (2002). Incorporating Learning Characteristics into an Intelligent Tutor.

Paper presented at the Sixth International Conference on Intelligent Tutoring Systems, ITS'02., Biarritz, France, 729-738.

Kelly, D., & Tangney, B. (2003a). A Framework for using Multiple Intelligences in an Intelligent Tutoring System. Paper presented at the World Conference on Educational Multimedia, Hypermedia & Telecommunications. EDMedia'03, Honolulu, USA, 2423-2430.

Kelly, D., & Tangney, B. (2003b). Learner’s responses to Multiple Intelligence Differentiated Instructional Material in an Intelligent Tutoring System. Paper presented at the Eleventh International Conference on Artificial Intelligence in Education, AIED’03, Sydney, Australia, 446-448.

Kelly, D., & Tangney, B. (2004a). Empirical Evaluation of an Adaptive Multiple Intelligence Based Tutoring System. Paper presented at the Third International Conference on Adaptive Hypermedia and Adaptive Web Based Systems, AH'04, Eindhoven, Netherlands, 308-311.

Kelly, D., & Tangney, B. (2004b). Evaluating Presentation Strategy and Choice in an Adaptive Multiple Intelligence Based Tutoring System. Paper presented at the In Individual Differences Workshop: Third International Conference on Adaptive HyperMedia and Adaptive Web Based Systems, AH'04, Eindhoven,Netherlands, 97-106.

Kelly, D., & Tangney, B. (2004c). On Using Multiple Intelligences in a Web-based Educational System. Paper presented at the Fifth Annual Educational Technology Users Conference, EdTech'04, Tralee, Ireland.

Kelly, D., & Tangney, B. (2004d). Predicting Learning Characteristics in a Multiple Intelligence based Tutoring System. Paper presented at the Seventh International Conference on Intelligent Tutoring Systems, ITS'04, Maceio, Brazil, 679-688.

Kelly, D., & Tangney, B. (2005a). Adapting to Intelligence Profile in an Adaptive Educational System. Journal Interacting With Computers, in press.

Kelly, D., & Tangney, B. (2005b). Do Learning Styles Matter? Paper presented at the Sixth Annual Educational Technology Users Conference, EdTech'05, Dublin, Ireland.

Kelly, D., & Tangney, B. (2005c). Matching and Mismatching Learning Characteristics with Multiple Intelligence Based Content. Paper presented at the Twelveth International Conference on Artificial Intelligence in Education, AIED'05, Amsterdam, Netherlands, 354-361.

Kelly, D., Weibelzahl, S., O’Loughlin, E., Pathak, P., Sanchez, I., & Gledhill, V. (2005b). eLearning Research and Development Roadmap for Ireland, e-Learning Research Agenda Forum, Sponsored by Science Foundation Ireland. Dublin.

Stynes, P., Kelly, D., & Durnin, S. (2004). Designing a learner-centred educational environment to achieve learner potential. Paper presented at the Fifth Annual Educational Technology Users Conference, EdTech'04, Tralee, Ireland.

–  –  –

1 INTRODUCTION

1.1 MOTIVATION

1.2 ADAPTING TO INDIVIDUAL DIFFERENCES

1.3 EDUCE ADAPTIVE EDUCATIONAL SYSTEM

1.4 RESEARCH GOALS AND CONTRIBUTIONS

1.5 STRUCTURE OF THE DISSERTATION

2 BACKGROUND AND RELATED WORK

2.1 INTRODUCTION

2.2 LEARNING THEORY AND INDIVIDUAL DIFFERENCES

2.2.1 Individual Difference Frameworks

2.2.2 Abilities and Intelligence

2.2.3 Learning Style

2.2.4 Individual Differences: Summary

2.3 TECHNOLOGY ENHANCED LEARNING ENVIRONMENTS

2.3.1 Overview of Adaptive and Intelligent Systems

2.3.2 Adaptive Hypermedia

2.3.3 Empirical Studies on Learning Styles

2.3.4 Adaptive Educational Systems

2.3.5 Technology Enhanced Learning: Summary

2.4 CONCLUSIONS

2.4.1 Individual Differences and Technology Enhanced Learning................ 52 2.4.2 Multiple Intelligences and Learning Styles

2.4.3 Intelligent Techniques for Diagnosis and Adaptation

2.4.4 Research Challenges

2.5 SUMMARY

3 EDUCE

3.1 INTRODUCTION

3.2 OVERALL ARCHITECTURE

3.3 MULTIPLE INTELLIGENCES

3.4 MI ASSESSMENT: MIDAS

3.5 DOMAIN MODEL

3.6 STUDENT MODEL

3.7 PRESENTATION MODULE

3.8 PREDICTIVE ENGINE

3.9 PEDAGOGICAL MANAGER

3.10 TECHNICAL IMPLEMENTATION

3.11 SUMMARY

4 PREDICTIVE ENGINE

4.1 INTRODUCTION

4.2 PREDICTIVE STATISTICAL MODELS

4.3 NAÏVE BAYES

4.4 ENGINE ARCHITECTURE

4.5 INPUT MODEL AND RESOURCE CLASSIFICATION

4.6 LEARNING SCHEME

4.7 SUMMARY

ix 5 VALIDATION

5.1 INTRODUCTION

5.2 CONTENT VALIDATION

5.2.1 Procedure

5.2.2 Ratings

5.2.3 Conclusions

5.3 PREDICTIVE ENGINE VALIDATION

5.3.1 Data Collection

5.3.2 Evaluation

5.3.3 Conclusions

5.4 SUMMARY

6 EXPERIMENTAL DESIGN

6.1 INTRODUCTION

6.2 EXPERIMENTAL DESIGN

6.3 EXPERIMENTAL PROCEDURE

6.4 TRACKING DATA

6.4.1 Participant Background

6.4.2 MIDAS MI Profile

6.4.3 Relative Gain

6.4.4 Activity Level and Activity Groups

6.4.5 Categories of Resources

6.4.6 Qualitative Feedback

6.5 PARTICIPANTS

6.6 SUMMARY

7 RESULTS

7.1 INTRODUCTION

7.2 STUDY A: ADAPTIVE DYNAMIC VERSUS LEARNER CONTROL

7.2.1 Influence of Different Tutorials

7.2.2 Choice and presentation strategy

7.2.3 Learning activity

7.2.4 Time-on-Task

7.2.5 Students with Medium Activity Levels

7.2.6 MI Profile and Performance

7.2.7 MI Profile: MIDAS vs. Behaviour

7.2.8 Resources Used

7.2.9 Qualitative Feedback

7.2.10 Summary

7.3 STUDY B: ADAPTIVE CONTROL

7.3.1 Choice and presentation strategy

7.3.2 Learning activity

7.3.3 Time-on-Task

7.3.4 Students with Low Activity Levels

7.3.5 MI Profile

7.3.6 Resources Used

7.3.7 Qualitative Feedback

7.3.8 Summary

7.4 DISCUSSION

8 CONCLUSIONS

8.1 INTRODUCTION

x

8.2 SUMMARY OF RESEARCH FINDINGS

8.2.1 Multiple Intelligences

8.2.2 Dynamic Diagnosis

8.2.3 Pedagogical Strategies

8.3 LIMITATIONS OF WORK

8.4 DIRECTIONS FOR FUTURE RESEARCH

8.4.1 Multiple Intelligences

8.4.2 Dynamic Diagnosis

8.4.3 Pedagogical Strategies

8.5 CONCLUSIONS

APPENDIX

A. NAÏVE BAYES ALGORITHM

B. QUESTIONNAIRES

B.1 Pre- and Post-Tests

B.2 Reflection during tutorial

B.3 Reflection after tutorial

B.4 MIDAS Questionnaire

C. EDUCE IMPLEMENTATION

C.1 Domain Knowledge Representation

C.2 Presentation Model

C.3 Pedagogical Model

C.4 Predictive Engine

D. PREDICTIVE ENGINE – SAMPLE OUTPUT

BIBLIOGRAPHY

xi List of Figures FIGURE 1-1: EDUCE ARCHITECTURE

FIGURE 2-1: THE TAXONOMY OF ADAPTIVE HYPERMEDIA TECHNOLOGIES, (ADAPTED

FROM BRUSILOVSKY, 2001)

FIGURE 3-1: EDUCE ARCHITECTURE

FIGURE 3-2: PEDAGOGICAL TAXONOMY FOR DEVELOPING MI MATERIAL

FIGURE 3-3: VERBAL/LINGUISTIC INTELLIGENCE

FIGURE 3-4: VERBAL/LINGUISTIC INTELLIGENCE

FIGURE 3-5: LOGICAL/MATHEMATICAL INTELLIGENCE

FIGURE 3-6: LOGICAL/MATHEMATICAL INTELLIGENCE

FIGURE 3-7: VISUAL/SPATIAL INTELLIGENCE

FIGURE 3-8: VISUAL/SPATIAL INTELLIGENCE

FIGURE 3-9: MUSICAL/RHYTHMIC INTELLIGENCE

FIGURE 3-10: MUSICAL/RHYTHMIC INTELLIGENCE

FIGURE 3-11: EVENTS IN PRESENTATION MODULE



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