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Title: Adaptive item sequencing in item-based learning environments
Authors: Wauters, Kelly
Issue Date: 19-Apr-2012
Abstract: In order to make computer-based learning environments more efficient, researchers have been exploring the possibility of an automatic adaptation of the learning environment to the learner’s needs and preferences (Wasson, 1993). In this dissertation, we focus on dynamic, item-based adaptive learning environments in which the item difficulty level and the learner’s ability level are assessed to enable adaptive item sequencing and adaptive feedback. Adaptive item sequencing is well-established in computerized adaptive testing (CAT; Wainer, 2000), which often makes use of the item response theory (IRT; Hambleton, Swaminathan, & Rogers, 1991). IRT expresses the probability of observing a particular response to an item as a function of certain characteristics of the item (e.g., item difficulty) and the person (e.g., knowledge level). We will explore how applying IRT can be helpful for adaptive item selection in learning environments, which could yield an increase in motivation, as is found in testing environments (Wainer, 2000) and enhance learning. In addition to that, we investigate how the use of IRT can help in providing detailed feedback to learners about his or her (progress in) knowledge level and the characteristics of the items. Furthermore, we examine the adequacy of some alternative measurement methods, incorporating an IRT model and otherwise.Hence, the aim of this dissertation is to unravel the possibilities and challenges that come together with extrapolating the ideas of CAT and IRT to item-based learning environments. By doing so we could provide the optimal learning path for each learner by selecting the problem that maximizes learning based on the learner’s current knowledge level and the difficulty level of the problem and adapting the feedback accordingly. After a general introduction (chapter 1), this dissertation provides a critical description of the possibilities and problems that may occur when applying IRT for adaptive item selection in learning environments, starting from existing research literature about electronic learning and testing environments (chapter 2). This analysis results in the identification of three concrete components of such personalized instruction: assessing the difficulty level of each item, assessing the learner’s current ability level and optimizing the interaction between the item and the learner.Part I of this dissertation (chapter 3) focuses on the first challenge, which is the assessment of the difficulty level of each item. We consider both response data (i.e. the correctness of the response to an item) and judgment data (i.e. learner feedback and expert rating) for the estimation of the item difficulty. Part II of this dissertation tackles the second challenge, which is the assessment of the learner’s current ability level. This part embodies two aspects of ability estimation in learning environments: the estimation of the learner’s initial ability level (chapter 4) and the tracking or following of the intra-individual change in ability level (chapter 5).Part III of this dissertation sheds light on the third challenge, which is the optimization of the interaction between the item and the learner. Attention is drawn to the item sequencing algorithm (chapter 6) and to the notion of adaptive item feedback (chapter 7).To conclude (chapter 8), we discuss the findings of this dissertation and define new research lines. This dissertation adds to the realization of adaptive item sequencing in item-based learning environments through the evaluation of the adequacy of IRT and alternative measurement methods and through the study of a specific item sequencing algorithm and an adaptive feedback approach.
Table of Contents: CHAPTER ONE - THE PUZZLE PIECES INTRODUCED 1

1 DEFINING THE STUDY DOMAIN 2
2 THE PUZZLE PIECES 4
3 THE PUZZLE 13
4 STRUCTURE OF THIS DISSERTATION 22


CHAPTER TWO - IDENTIFYING MERITS & DRAWBACKS IN IRT APPLICATION 25

1 INTRODUCTION 26
2 ADAPTIVE ITEM SEQUENCING IN TESTING ENVIRONMENTS 29
3 ADAPTIVE ITEM SEQUENCING IN LEARNING ENVIRONMENTS 30
4 DISCUSSION AND CONCLUSION 42


PART ONE THE DIFFICULTY OF ITEM DIFFICULTY ESTIMATION 49


CHAPTER THREE - DATA AND JUDGMENT COMBINED 51

1 INTRODUCTION 52
2 RELATED WORK ON ITEM DIFFICULTY ESTIMATION METHODS 54
3 METHODOLOGY 63
4 RESULTS 67
5 DISCUSSION AND CONCLUSION 75


PART TWO THE CAPABILITY OF ABILITY TRACKING 81


CHAPTER FOUR - A POSITIVE VIEW ON GROUPING 83

1 INTRODUCTION 84
2 METHODOLOGY 92
3 RESULTS 94
4 DISCUSSION AND CONCLUSION 100


CHAPTER FIVE - HOW TO TRACK LEARNING 105

1 INTRODUCTION 106
2 METHODOLOGY 112
3 RESULTS 118
4 DISCUSSION AND CONCLUSION 120


PART THREE JOINING FORCES: MAKING ONE AND ONE EQUAL THREE 123


CHAPTER SIX - ITEM SEQUENCING ALGORITHM 125

1 INTRODUCTION 126
2 ADAPTIVE ITEM SEQUENCING IN ITEM-BASED LEARNING ENVIRONMENTS 128
3 EXPERIMENT 132
4 DISCUSSION 143


CHAPTER SEVEN - ADAPTIVE FEEDBACK 147

1 (ADAPTIVE) FEEDBACK IN ELECTRONIC LEARNING ENVIRONMENTS 148
2 EXPERIMENT 151
3 DISCUSSION 159


CHAPTER EIGHT - CHALLENGING PUZZLES AND PUZZLING CHALLENGES 163

1 CHALLENGING PUZZLE 165
2 REFLECTING THE PUZZLE 169
3 PUZZLING CHALLENGES 176
4 GENERAL CONCLUSION 188


REFERENCES 189


ADDENDA 224

ADDENDUM A: LEARNING ENVIRONMENT 225
ADDENDUM B: QUESTIONNAIRES 229
ISBN: 978-94-6197-024-4
Publication status: published
KU Leuven publication type: TH
Appears in Collections:Faculty of Psychology and Educational Sciences, Campus Kulak Kortrijk – miscellaneous
Leuven Statistics Research Centre (LStat)
Methodology of Educational Sciences
Comparative, Historical and Applied Linguistics, Leuven
Faculty of Arts, Campus Kulak Kortrijk

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