{"product_id":"data-mining-and-learning-analytics-9781118998236","title":"Data Mining and Learning Analytics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eAddresses the impacts of data mining on education and reviews applications in educational research teaching, and learning\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis 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 a\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eNotes on Contributors xi\u003c\/p\u003e \u003cp\u003eIntroduction: Education At Computational Crossroads xxiii\u003cbr\u003e\u003ci\u003eSamira ElAtia, Donald Ipperciel, and Osmar R. Zaïane\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I \u003c\/b\u003e\u003cb\u003eAt The Intersection of Two Fields: EDM 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 \u003c\/b\u003e\u003cb\u003eEducational Process Mining: A Tutorial and Case Study Using Moodle Data Sets 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eCristóbal Romero, Rebeca Cerezo, Alejandro Bogarín, and Miguel Sanchez‐Santillán\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Background 5\u003c\/p\u003e \u003cp\u003e1.2 Data Description and Preparation 7\u003c\/p\u003e \u003cp\u003e1.2.1 Preprocessing Log Data 7\u003c\/p\u003e \u003cp\u003e1.2.2 Clustering Approach for Grouping Log Data 11\u003c\/p\u003e \u003cp\u003e1.3 Working with ProM 16\u003c\/p\u003e \u003cp\u003e1.3.1 Discovered Models 19\u003c\/p\u003e \u003cp\u003e1.3.2 Analysis of the Models’ Performance 23\u003c\/p\u003e \u003cp\u003e1.4 Conclusion 26\u003c\/p\u003e \u003cp\u003eAcknowledgments 27\u003c\/p\u003e \u003cp\u003eReferences 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 \u003c\/b\u003e\u003cb\u003eOn Big Data And Text Mining in the Humanities29\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGeoffrey Rockwell and Bettina Berendt\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Busa and the Digital Text 30\u003c\/p\u003e \u003cp\u003e2.2 Thesaurus Linguae Graecae and the Ibycus Computer as Infrastructure 32\u003c\/p\u003e \u003cp\u003e2.2.1 Complete Data Sets 33\u003c\/p\u003e \u003cp\u003e2.3 Cooking with Statistics 35\u003c\/p\u003e \u003cp\u003e2.4 Conclusions 37\u003c\/p\u003e \u003cp\u003eReferences 38\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 \u003c\/b\u003e\u003cb\u003eFinding Predictors in Higher Education41\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDavid Eubanks, William Evers Jr., and Nancy Smith\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Contrasting Traditional and Computational Methods 42\u003c\/p\u003e \u003cp\u003e3.2 Predictors and Data Exploration 45\u003c\/p\u003e \u003cp\u003e3.3 Data Mining Application: An Example 50\u003c\/p\u003e \u003cp\u003e3.4 Conclusions 52\u003c\/p\u003e \u003cp\u003eReferences 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 \u003c\/b\u003e\u003cb\u003eEducational Data Mining: A MOOC Experience55\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRyan 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\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Big Data in Education: The Course 55\u003c\/p\u003e \u003cp\u003e4.1.1 Iteration 1: Coursera 55\u003c\/p\u003e \u003cp\u003e4.1.2 Iteration 2: edX 56\u003c\/p\u003e \u003cp\u003e4.2 Cognitive Tutor Authoring Tools 57\u003c\/p\u003e \u003cp\u003e4.3 Bazaar 58\u003c\/p\u003e \u003cp\u003e4.4 Walkthrough 58\u003c\/p\u003e \u003cp\u003e4.4.1 Course Content 58\u003c\/p\u003e \u003cp\u003e4.4.2 Research on BDEMOOC 61\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 65\u003c\/p\u003e \u003cp\u003eAcknowledgments 65\u003c\/p\u003e \u003cp\u003eReferences 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 \u003c\/b\u003e\u003cb\u003eData Mining and Action Research 67\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEllina Chernobilsky, Edith Ries, and Joanne Jasmine\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Process 69\u003c\/p\u003e \u003cp\u003e5.2 Design Methodology 71\u003c\/p\u003e \u003cp\u003e5.3 Analysis and Interpretation of Data 72\u003c\/p\u003e \u003cp\u003e5.3.1 Quantitative Data Analysis and Interpretation 73\u003c\/p\u003e \u003cp\u003e5.3.2 Qualitative Data Analysis and Interpretation 74\u003c\/p\u003e \u003cp\u003e5.4 Challenges 75\u003c\/p\u003e \u003cp\u003e5.5 Ethics 76\u003c\/p\u003e \u003cp\u003e5.6 Role of Administration in the Data Collection Process 76\u003c\/p\u003e \u003cp\u003e5.7 Conclusion 77\u003c\/p\u003e \u003cp\u003eReferences 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II \u003c\/b\u003e\u003cb\u003ePedagogical Applications of EDM\u003c\/b\u003e\u003cb\u003e79\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 \u003c\/b\u003e\u003cb\u003eDesign of an Adaptive Learning System and Educational Data Mining81\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eZhiyong Liu and Nick Cercone\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Dimensionalities of the User Model in ALS 83\u003c\/p\u003e \u003cp\u003e6.2 Collecting Data for ALS 85\u003c\/p\u003e \u003cp\u003e6.3 Data Mining in ALS 86\u003c\/p\u003e \u003cp\u003e6.3.1 Data Mining for User Modeling 87\u003c\/p\u003e \u003cp\u003e6.3.2 Data Mining for Knowledge Discovery 88\u003c\/p\u003e \u003cp\u003e6.4 ALS Model and Function Analyzing 90\u003c\/p\u003e \u003cp\u003e6.4.1 Introduction of Module Functions 90\u003c\/p\u003e \u003cp\u003e6.4.2 Analyzing the Workflow 93\u003c\/p\u003e \u003cp\u003e6.5 Future Works 94\u003c\/p\u003e \u003cp\u003e6.6 Conclusions 94\u003c\/p\u003e \u003cp\u003eAcknowledgment 95\u003c\/p\u003e \u003cp\u003eReferences 95\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 \u003c\/b\u003e\u003cb\u003eThe “Geometry” of Naive Bayes: Teaching Probabilities by “Drawing” Them99\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGiorgio Maria Di Nunzio\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 99\u003c\/p\u003e \u003cp\u003e7.1.1 Main Contribution 100\u003c\/p\u003e \u003cp\u003e7.1.2 Related Works 101\u003c\/p\u003e \u003cp\u003e7.2 The Geometry of NB Classification 102\u003c\/p\u003e \u003cp\u003e7.2.1 Mathematical Notation 102\u003c\/p\u003e \u003cp\u003e7.2.2 Bayesian Decision Theory 103\u003c\/p\u003e \u003cp\u003e7.3 Two-Dimensional Probabilities 105\u003c\/p\u003e \u003cp\u003e7.3.1 Working with Likelihoods and Priors Only 107\u003c\/p\u003e \u003cp\u003e7.3.2 De‐normalizing Probabilities 108\u003c\/p\u003e \u003cp\u003e7.3.3 NB Approach 109\u003c\/p\u003e \u003cp\u003e7.3.4 Bernoulli Naïve Bayes 110\u003c\/p\u003e \u003cp\u003e7.4 A New Decision Line: Far from the Origin 111\u003c\/p\u003e \u003cp\u003e7.4.1 De‐normalization Makes (Some) Problems Linearly Separable 112\u003c\/p\u003e \u003cp\u003e7.5 Likelihood Spaces, When Logarithms make a Difference (or a SUM) 114\u003c\/p\u003e \u003cp\u003e7.5.1 De‐normalization Makes (Some) Problems Linearly Separable 115\u003c\/p\u003e \u003cp\u003e7.5.2 A New Decision in Likelihood Spaces 116\u003c\/p\u003e \u003cp\u003e7.5.3 A Real Case Scenario: Text Categorization 117\u003c\/p\u003e \u003cp\u003e7.6 Final Remarks 118\u003c\/p\u003e \u003cp\u003eReferences 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 \u003c\/b\u003e\u003cb\u003eExamining the Learning Networks of a MOOC121\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMeaghan Brugha and Jean‐Paul Restoule\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Review of Literature 122\u003c\/p\u003e \u003cp\u003e8.2 Course Context 124\u003c\/p\u003e \u003cp\u003e8.3 Results and Discussion 125\u003c\/p\u003e \u003cp\u003e8.4 Recommendations for Future Research 133\u003c\/p\u003e \u003cp\u003e8.5 Conclusions 134\u003c\/p\u003e \u003cp\u003eReferences 135\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 \u003c\/b\u003e\u003cb\u003eExploring the Usefulness of Adaptive ELearning Laboratory Environments in Teaching Medical Science139\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eThuan Thai and Patsie Polly\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 139\u003c\/p\u003e \u003cp\u003e9.2 Software for Learning and Teaching 141\u003c\/p\u003e \u003cp\u003e9.2.1 Reflective Practice: ePortfolio 141\u003c\/p\u003e \u003cp\u003e9.2.2 Online Quizzes 143\u003c\/p\u003e \u003cp\u003e9.2.3 Online Practical Lessons 144\u003c\/p\u003e \u003cp\u003e9.2.4 Virtual Laboratories 145\u003c\/p\u003e \u003cp\u003e9.2.5 The Gene Suite 147\u003c\/p\u003e \u003cp\u003e9.3 Potential Limitations 152\u003c\/p\u003e \u003cp\u003e9.4 Conclusion 153\u003c\/p\u003e \u003cp\u003eAcknowledgments 153\u003c\/p\u003e \u003cp\u003eReferences 154\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 \u003c\/b\u003e\u003cb\u003eInvestigating Co‐Occurrence Patterns of Learners’ Grammatical Errors across Proficiency Levels and Essay Topics Based on Association Analysis 157\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eYutaka Ishii\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 157\u003c\/p\u003e \u003cp\u003e10.1.1 The Relationship between Data Mining and Educational Research 157\u003c\/p\u003e \u003cp\u003e10.1.2 English Writing Instruction in the Japanese Context 158\u003c\/p\u003e \u003cp\u003e10.2 Literature Review 159\u003c\/p\u003e \u003cp\u003e10.3 Method 160\u003c\/p\u003e \u003cp\u003e10.3.1 Konan‐JIEM Learner Corpus 160\u003c\/p\u003e \u003cp\u003e10.3.2 Association Analysis 162\u003c\/p\u003e \u003cp\u003e10.4 Experiment 1 162\u003c\/p\u003e \u003cp\u003e10.5 Experiment 2 163\u003c\/p\u003e \u003cp\u003e10.6 Discussion and Conclusion 164\u003c\/p\u003e \u003cp\u003eAppendix A: Example of Learner’s Essay (University Life) 164\u003c\/p\u003e \u003cp\u003eAppendix B: Support Values of all Topics 165\u003c\/p\u003e \u003cp\u003eAppendix C: Support Values of Advanced, Intermediate, and Beginner Levels of Learners 168\u003c\/p\u003e \u003cp\u003eReferences 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III \u003c\/b\u003e\u003cb\u003eEDM and Educational Research 173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 \u003c\/b\u003e\u003cb\u003eMining Learning Sequences in MOOCs: Does Course Design Constrain Students’ Behaviors Or Do Students Shape Their Own Learning? 175\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eLorenzo Vigentini, Simon McIntyre, Negin Mirriahi, and Dennis Alonzo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 175\u003c\/p\u003e \u003cp\u003e11.1.1 Perceptions and Challenges of MOOC Design 176\u003c\/p\u003e \u003cp\u003e11.1.2 What Do We Know About Participants’ Navigation: Choice and Control 177\u003c\/p\u003e \u003cp\u003e11.2 Data Mining in MOOCs: Related Work 178\u003c\/p\u003e \u003cp\u003e11.2.1 Setting the Hypotheses 179\u003c\/p\u003e \u003cp\u003e11.3 The Design and Intent of the LTTO MOOC 180\u003c\/p\u003e \u003cp\u003e11.3.1 Course Grading and Certification 183\u003c\/p\u003e \u003cp\u003e11.3.2 Delivering the Course 183\u003c\/p\u003e \u003cp\u003e11.3.3 Operationalize Engagement, Personal Success, and Course Success in LTTO 184\u003c\/p\u003e \u003cp\u003e11.4 Data Analysis 184\u003c\/p\u003e \u003cp\u003e11.4.1 Approaches to Process the Data Sources 185\u003c\/p\u003e \u003cp\u003e11.4.2 LTTO in Numbers 186\u003c\/p\u003e \u003cp\u003e11.4.3 Characterizing Patterns of Completion and Achievement 186\u003c\/p\u003e \u003cp\u003e11.4.4 Redefining Participation and Engagement 189\u003c\/p\u003e \u003cp\u003e11.5 Mining Behaviors and Intents 191\u003c\/p\u003e \u003cp\u003e11.5.1 Participants’ Intent and Behaviors: A Classification Model 191\u003c\/p\u003e \u003cp\u003e11.5.2 Natural Clustering Based on Behaviors 194\u003c\/p\u003e \u003cp\u003e11.5.3 Stated Intents and Behaviors: Are They Related? 198\u003c\/p\u003e \u003cp\u003e11.6 Closing the Loop: Informing Pedagogy and Course Enhancement 198\u003c\/p\u003e \u003cp\u003e11.6.1 Conclusions, Lessons Learnt, and Future Directions 200\u003c\/p\u003e \u003cp\u003eReferences 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 \u003c\/b\u003e\u003cb\u003eUnderstanding Communication Patterns in MOOCs: Combining Data Mining and Qualitative Methods 207\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRebecca Eynon, Isis Hjorth, Taha Yasseri, and Nabeel Gillani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 207\u003c\/p\u003e \u003cp\u003e12.2 Methodological Approaches to Understanding Communication Patterns in MOOCs 209\u003c\/p\u003e \u003cp\u003e12.3 Description 210\u003c\/p\u003e \u003cp\u003e12.3.1 Structural Connections 211\u003c\/p\u003e \u003cp\u003e12.4 Examining Dialogue 213\u003c\/p\u003e \u003cp\u003e12.5 Interpretative Models 214\u003c\/p\u003e \u003cp\u003e12.6 Understanding Experience 215\u003c\/p\u003e \u003cp\u003e12.7 Experimentation 216\u003c\/p\u003e \u003cp\u003e12.8 Future Research 217\u003c\/p\u003e \u003cp\u003eReferences 218\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 \u003c\/b\u003e\u003cb\u003eAn Example of Data Mining: Exploring The Relationship Between Applicant Attributes and Academic Measures of Success in a Pharmacy Program 223\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDion Brocks and Ken Cor\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 223\u003c\/p\u003e \u003cp\u003e13.2 Methods 225\u003c\/p\u003e \u003cp\u003e13.3 Results 228\u003c\/p\u003e \u003cp\u003e13.4 Discussion 230\u003c\/p\u003e \u003cp\u003e13.4.1 Prerequisite Predictors 230\u003c\/p\u003e \u003cp\u003e13.4.2 Demographic Predictors 232\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 234\u003c\/p\u003e \u003cp\u003eAppendix A 234\u003c\/p\u003e \u003cp\u003eReferences 236\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 \u003c\/b\u003e\u003cb\u003eA New Way of Seeing: Using a Data Mining Approach to Understand Children’s Views of Diversity and “Difference” in Picture Books237\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRobin A. Moeller and Hsin‐liang Chen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 237\u003c\/p\u003e \u003cp\u003e14.2 Study 1: Using Data Mining to Better Understand Perceptions of Race 238\u003c\/p\u003e \u003cp\u003e14.2.1 Background 238\u003c\/p\u003e \u003cp\u003e14.2.2 Research Questions 239\u003c\/p\u003e \u003cp\u003e14.2.3 Methods 240\u003c\/p\u003e \u003cp\u003e14.2.4 Findings 240\u003c\/p\u003e \u003cp\u003e14.2.5 Discussion 248\u003c\/p\u003e \u003cp\u003e14.3 Study 2: Translating Data Mining Results to Picture Book Concepts of “Difference” 248\u003c\/p\u003e \u003cp\u003e14.3.1 Background 248\u003c\/p\u003e \u003cp\u003e14.3.2 Research Questions 249\u003c\/p\u003e \u003cp\u003e14.3.3 Methodology 250\u003c\/p\u003e \u003cp\u003e14.3.4 Findings 250\u003c\/p\u003e \u003cp\u003e14.3.5 Discussion and Implications 252\u003c\/p\u003e \u003cp\u003e14.4 Conclusions 252\u003c\/p\u003e \u003cp\u003eReferences 252\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 \u003c\/b\u003e\u003cb\u003eData Mining with Natural Language Processing and Corpus Linguistics: Unlocking Access to School Children’s Language in Diverse Contexts to Improve Instructional and Assessment Practices\u003c\/b\u003e\u003cb\u003e255\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAlison L. Bailey, Anne Blackstock‐Bernstein, Eve Ryan, and Despina Pitsoulakis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 255\u003c\/p\u003e \u003cp\u003e15.2 Identifying the Problem 256\u003c\/p\u003e \u003cp\u003e15.3 Use of Corpora and Technology in Language Instruction and Assessment 261\u003c\/p\u003e \u003cp\u003e15.3.1 Language Corpora in ESL and EFL Teaching and Learning 261\u003c\/p\u003e \u003cp\u003e15.3.2 Previous Extensions of Corpus Linguistics to School‐Age Language 262\u003c\/p\u003e \u003cp\u003e15.3.3 Corpus Linguistics in Language Assessment 263\u003c\/p\u003e \u003cp\u003e15.3.4 Big Data Purposes, Techniques, and Technology 264\u003c\/p\u003e \u003cp\u003e15.4 Creating a School‐Age Learner Corpus and Digital Data Analytics System 266\u003c\/p\u003e \u003cp\u003e15.4.1 Language Measures Included in DRGON 267\u003c\/p\u003e \u003cp\u003e15.4.2 The DLLP as a Promising Practice 268\u003c\/p\u003e \u003cp\u003e15.5 Next Steps, “Modest Data,” and Closing Remarks 269\u003c\/p\u003e \u003cp\u003eAcknowledgments 271\u003c\/p\u003e \u003cp\u003eAppendix A: Examples of Oral and Written Explanation Elicitation Prompts 272\u003c\/p\u003e \u003cp\u003eReferences 272\u003c\/p\u003e \u003cp\u003eIndex 277\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406964728151,"sku":"9781118998236","price":98.06,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118998236.jpg?v=1730497716","url":"https:\/\/bookcurl.com\/products\/data-mining-and-learning-analytics-9781118998236","provider":"Book Curl","version":"1.0","type":"link"}