Data Mining and Learning Analytics

NOTES ON CONTRIBUTORS xi INTRODUCTION: EDUCATION AT COMPUTATIONAL CROSSROADS xxiii Samira ElAtia, Donald Ipperciel, and Osmar R. Zaiane 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 Cristobal Romero, Rebeca Cerezo, Alejandro Bogarin, and Miguel Sanchez -Santillan 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 Rose, Gaurav Singh Tomar, Oliver Ferschke, Jing Zhang, Michael J. Cennamo, Stephanie Ogden, Therese Condit, Jose 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 NAIVE 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 Naive 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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