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Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning

This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining's four guiding principles- prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM's emerging role in helping to advance educational research-from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields.

Includes case studies where data mining techniques have been effectively applied to advance teaching and learning
Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students
Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students
Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics
Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.



TABLE OF CONTENTS
Notes on Contributors xi

Introduction: Education At Computational Crossroads xxiii
Samira ElAtia, Donald Ipperciel, and Osmar R. Zaïane

Part I At The Intersection of Two Fields: EDM 1

Chapter 1 Educational Process Mining: A Tutorial and Case Study Using Moodle Data Sets 3
Cristóbal Romero, Rebeca Cerezo, Alejandro Bogarín, and Miguel Sanchez-Santillán

1.1 Background 5

1.2 Data Description and Preparation 7

1.2.1 Preprocessing Log Data 7

1.2.2 Clustering Approach for Grouping Log Data 11

1.3 Working with ProM 16

1.3.1 Discovered Models 19

1.3.2 Analysis of the Models' Performance 23

1.4 Conclusion 26

Acknowledgments 27

References 27

Chapter 2 On Big Data And Text Mining in the Humanities29
Geoffrey Rockwell and Bettina Berendt

2.1 Busa and the Digital Text 30

2.2 Thesaurus Linguae Graecae and the Ibycus Computer as Infrastructure 32

2.2.1 Complete Data Sets 33

2.3 Cooking with Statistics 35

2.4 Conclusions 37

References 38

Chapter 3 Finding Predictors in Higher Education41
David Eubanks, William Evers Jr., and Nancy Smith

3.1 Contrasting Traditional and Computational Methods 42

3.2 Predictors and Data Exploration 45

3.3 Data Mining Application: An Example 50

3.4 Conclusions 52

References 53

Chapter 4 Educational Data Mining: A MOOC Experience55
Ryan S. Baker, Yuan Wang, Luc Paquette, Vincent Aleven, Octav Popescu, Jonathan Sewall, Carolyn Rosé, Gaurav Singh Tomar, Oliver Ferschke, Jing Zhang, Michael J. Cennamo, Stephanie Ogden, Therese Condit, José Diaz, Scott Crossley, Danielle S. McNamara, Denise K. Comer, Collin F. Lynch, Rebecca Brown, Tiffany Barnes, and Yoav Bergner

4.1 Big Data in Education: The Course 55

4.1.1 Iteration 1: Coursera 55

4.1.2 Iteration 2: edX 56

4.2 Cognitive Tutor Authoring Tools 57

4.3 Bazaar 58

4.4 Walkthrough 58

4.4.1 Course Content 58

4.4.2 Research on BDEMOOC 61

4.5 Conclusion 65

Acknowledgments 65

References 65

Chapter 5 Data Mining and Action Research 67
Ellina Chernobilsky, Edith Ries, and Joanne Jasmine

5.1 Process 69

5.2 Design Methodology 71

5.3 Analysis and Interpretation of Data 72

5.3.1 Quantitative Data Analysis and Interpretation 73

5.3.2 Qualitative Data Analysis and Interpretation 74

5.4 Challenges 75

5.5 Ethics 76

5.6 Role of Administration in the Data Collection Process 76

5.7 Conclusion 77

References 77

Part II Pedagogical Applications of EDM79

Chapter 6 Design of an Adaptive Learning System and Educational Data Mining81
Zhiyong Liu and Nick Cercone

6.1 Dimensionalities of the User Model in ALS 83

6.2 Collecting Data for ALS 85

6.3 Data Mining in ALS 86

6.3.1 Data Mining for User Modeling 87

6.3.2 Data Mining for Knowledge Discovery 88

6.4 ALS Model and Function Analyzing 90

6.4.1 Introduction of Module Functions 90

6.4.2 Analyzing the Workflow 93

6.5 Future Works 94

6.6 Conclusions 94

Acknowledgment 95

References 95

Chapter 7 The "Geometryö of Naive Bayes: Teaching Probabilities by "Drawingö Them99
Giorgio Maria Di Nunzio

7.1 Introduction 99

7.1.1 Main Contribution 100

7.1.2 Related Works 101

7.2 The Geometry of NB Classification 102

7.2.1 Mathematical Notation 102

7.2.2 Bayesian Decision Theory 103

7.3 Two-Dimensional Probabilities 105

7.3.1 Working with Likelihoods and Priors Only 107

7.3.2 De-normalizing Probabilities 108

7.3.3 NB Approach 109

7.3.4 Bernoulli Naïve Bayes 110

7.4 A New Decision Line: Far from the Origin 111

7.4.1 De-normalization Makes (Some) Problems Linearly Separable 112

7.5 Likelihood Spaces, When Logarithms make a Difference (or a SUM) 114

7.5.1 De-normalization Makes (Some) Problems Linearly Separable 115

7.5.2 A New Decision in Likelihood Spaces 116

7.5.3 A Real Case Scenario: Text Categorization 117

7.6 Final Remarks 118

References 119

Chapter 8 Examining the Learning Networks of a MOOC121
Meaghan Brugha and Jean-Paul Restoule

8.1 Review of Literature 122

8.2 Course Context 124

8.3 Results and Discussion 125

8.4 Recommendations for Future Research 133

8.5 Conclusions 134

References 135

Chapter 9 Exploring the Usefulness of Adaptive ELearning Laboratory Environments in Teaching Medical Science139
Thuan Thai and Patsie Polly

9.1 Introduction 139

9.2 Software for Learning and Teaching 141

9.2.1 Reflective Practice: ePortfolio 141

9.2.2 Online Quizzes 143

9.2.3 Online Practical Lessons 144

9.2.4 Virtual Laboratories 145

9.2.5 The Gene Suite 147

9.3 Potential Limitations 152

9.4 Conclusion 153

Acknowledgments 153

References 154

Chapter 10 Investigating Co-Occurrence Patterns of Learners' Grammatical Errors across Proficiency Levels and Essay Topics Based on Association Analysis 157
Yutaka Ishii

10.1 Introduction