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Data Mining and Learning Analytics: Applications in Educational Research

Samira ElAtia (Editor), Donald Ipperciel (Editor), Osmar R. Zaļane (Editor)
ISBN: 978-1-118-99823-6
320 pages
September 2016
Data Mining and Learning Analytics: Applications in Educational Research (1118998235) cover image

Description

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.

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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 Sánchez -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 HUMANITIES 29
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 EDUCATION 41
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 EXPERIENCE 55
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 EDM 79

CHAPTER 6 DESIGN OF AN ADAPTIVE LEARNING SYSTEM AND EDUCATIONAL DATA MINING 81
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 “GEOMETRY” OF NAÏVE BAYES: TEACHING PROBABILITIES BY “DRAWING” THEM 99
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 MOOC 121
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 SCIENCE 139
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 157

10.1.1 The Relationship between Data Mining and Educational Research 157

10.1.2 English Writing Instruction in the Japanese Context 158

10.2 Literature Review 159

10.3 Method 160

10.3.1 Konan -JIEM Learner Corpus 160

10.3.2 Association Analysis 162

10.4 Experiment 1 162

10.5 Experiment 2 163

10.6 Discussion and Conclusion 164

Appendix A: Example of Learner’s Essay (University Life) 164

Appendix B: Support Values of all Topics 165

Appendix C: Support Values of Advanced, Intermediate, and Beginner Levels of Learners 168

References 169

PART III EDM AND EDUCATIONAL RESEARCH 173

CHAPTER 11 MINING LEARNING SEQUENCES IN MOOCs: DOES COURSE DESIGN CONSTRAIN STUDENTS’ BEHAVIORS OR DO STUDENTS SHAPE THEIR OWN LEARNING? 175
Lorenzo Vigentini, Simon McIntyre, Negin Mirriahi, and Dennis Alonzo

11.1 Introduction 175

11.1.1 Perceptions and Challenges of MOOC Design 176

11.1.2 What Do We Know About Participants’ Navigation: Choice and Control 177

11.2 Data Mining in MOOCs: Related Work 178

11.2.1 Setting the Hypotheses 179

11.3 The Design and Intent of the LTTO MOOC 180

11.3.1 Course Grading and Certification 183

11.3.2 Delivering the Course 183

11.3.3 Operationalize Engagement, Personal Success, and Course Success in LTTO 184

11.4 Data Analysis 184

11.4.1 Approaches to Process the Data Sources 185

11.4.2 LTTO in Numbers 186

11.4.3 Characterizing Patterns of Completion and Achievement 186

11.4.4 Redefining Participation and Engagement 189

11.5 Mining Behaviors and Intents 191

11.5.1 Participants’ Intent and Behaviors: A Classification Model 191

11.5.2 Natural Clustering Based on Behaviors 194

11.5.3 Stated Intents and Behaviors: Are They Related? 198

11.6 Closing the Loop: Informing Pedagogy and Course Enhancement 198

11.6.1 Conclusions, Lessons Learnt, and Future Directions 200

References 201

CHAPTER 12 UNDERSTANDING COMMUNICATION PATTERNS IN MOOCs: COMBINING DATA MINING AND QUALITATIVE METHODS 207
Rebecca Eynon, Isis Hjorth, Taha Yasseri, and Nabeel Gillani

12.1 Introduction 207

12.2 Methodological Approaches to Understanding Communication Patterns in MOOCs 209

12.3 Description 210

12.3.1 Structural Connections 211

12.4 Examining Dialogue 213

12.5 Interpretative Models 214

12.6 Understanding Experience 215

12.7 Experimentation 216

12.8 Future Research 217

References 218

CHAPTER 13 AN EXAMPLE OF DATA MINING: EXPLORING THE RELATIONSHIP BETWEEN APPLICANT ATTRIBUTES AND ACADEMIC MEASURES OF SUCCESS IN A PHARMACY PROGRAM 223
Dion Brocks and Ken Cor

13.1 Introduction 223

13.2 Methods 225

13.3 Results 228

13.4 Discussion 230

13.4.1 Prerequisite Predictors 230

13.4.2 Demographic Predictors 232

13.5 Conclusion 234

Appendix A 234

References 236

CHAPTER 14 A NEW WAY OF SEEING: USING A DATA MINING APPROACH TO UNDERSTAND CHILDREN’S VIEWS OF DIVERSITY AND “DIFFERENCE” IN PICTURE BOOKS 237
Robin A. Moeller and Hsin -liang Chen

14.1 Introduction 237

14.2 Study 1: Using Data Mining to Better Understand Perceptions of Race 238

14.2.1 Background 238

14.2.2 Research Questions 239

14.2.3 Methods 240

14.2.4 Findings 240

14.2.5 Discussion 248

14.3 Study 2: Translating Data Mining Results to Picture Book Concepts of “Difference” 248

14.3.1 Background 248

14.3.2 Research Questions 249

14.3.3 Methodology 250

14.3.4 Findings 250

14.3.5 Discussion and Implications 252

14.4 Conclusions 252

References 252

CHAPTER 15 DATA MINING WITH NATURAL LANGUAGE PROCESSING AND CORPUS LINGUISTICS: UNLOCKING ACCESS TO SCHOOL CHILDREN’S LANGUAGE IN DIVERSE CONTEXTS TO IMPROVE INSTRUCTIONAL AND ASSESSMENT PRACTICES 255
Alison L. Bailey, Anne Blackstock -Bernstein, Eve Ryan, and Despina Pitsoulakis

15.1 Introduction 255

15.2 Identifying the Problem 256

15.3 Use of Corpora and Technology in Language Instruction and Assessment 261

15.3.1 Language Corpora in ESL and EFL Teaching and Learning 261

15.3.2 Previous Extensions of Corpus Linguistics to School -Age Language 262

15.3.3 Corpus Linguistics in Language Assessment 263

15.3.4 Big Data Purposes, Techniques, and Technology 264

15.4 Creating a School -Age Learner Corpus and Digital Data Analytics System 266

15.4.1 Language Measures Included in DRGON 267

15.4.2 The DLLP as a Promising Practice 268

15.5 Next Steps, “Modest Data,” and Closing Remarks 269

Acknowledgments 271

Apendix A: Examples of Oral and Written Explanation Elicitation Prompts 272

References 272

INDEX 277

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Author Information

Samira ElAtia is Associate Professor of Education at The University of Alberta, Canada. She has published numerous articles and book chapters on topics relating to the use of technology to support pedagogical research and education in higher education. Her current research focuses on using e-learning environment and big data for fair and valid longitudinal assessment of, and for, learning within higher education.

Donald Ipperciel is Principal and Professor at Glendon College, York University, Toronto, Canada and was the Canadian Research Chair in Political Philosophy and Canadian Studies between 2002 and 2012. He has authored several books and has contributed chapters and articles in more than 60 publications.  Ipperciel has dedicated many years of research on the questions of e-learning and using technology in education. He is co-editor of the Canadian Journal of Learning and Technology since 2010.

Osmar R. Zaiane is Professor of Computing Science at the University of Alberta, Canada and Scientific Director of the Alberta Innovates Centre of Machine Learning. A renowned researcher and computer scientist, Dr. Zaiane is former Secretary Treasurer of the Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data Mining. He obtained the IEEE ICDM Outstanding Service Aware in 2009 as well as the ACM SIGKDD Service Award the following year.

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