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2016016549 |
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9781118998236 (cloth) |
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9781118998205 |
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DLC |
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eng |
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eng. |
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LB1028.43 |
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.D385 2016 |
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370.72/7 |
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23 |
| 245 00 - TITLE STATEMENT |
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| Title |
Data mining and learning analytics : |
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applications in educational research / |
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edited by Samira ElAtia, Donald Ipperciel, Osmar R. Zaiane. |
| 264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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Hoboken, New Jersey : |
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John Wiley & Sons, Inc., |
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[2016] |
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1 online resource (xxviii, 283 pages) : |
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illustrations |
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computer |
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online resource |
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| 490 0# - SERIES STATEMENT |
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Wiley Series on Methods and Applications in Data Mining |
| 500 ## - GENERAL NOTE |
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| General note |
ABOUT THE AUTHOR<br/>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.<br/><br/>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.<br/><br/>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. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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Includes bibliographical references and index. |
| 505 ## - CONTENTS |
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TABLE OF CONTENTS<br/>Notes on Contributors xi<br/><br/>Introduction: Education At Computational Crossroads xxiii<br/>Samira ElAtia, Donald Ipperciel, and Osmar R. Zaïane<br/><br/>Part I At The Intersection of Two Fields: EDM 1<br/><br/>Chapter 1 Educational Process Mining: A Tutorial and Case Study Using Moodle Data Sets 3<br/>Cristóbal Romero, Rebeca Cerezo, Alejandro Bogarín, and Miguel Sanchez‐Santillán<br/><br/>1.1 Background 5<br/><br/>1.2 Data Description and Preparation 7<br/><br/>1.2.1 Preprocessing Log Data 7<br/><br/>1.2.2 Clustering Approach for Grouping Log Data 11<br/><br/>1.3 Working with ProM 16<br/><br/>1.3.1 Discovered Models 19<br/><br/>1.3.2 Analysis of the Models’ Performance 23<br/><br/>1.4 Conclusion 26<br/><br/>Acknowledgments 27<br/><br/>References 27<br/><br/>Chapter 2 On Big Data And Text Mining in the Humanities29<br/>Geoffrey Rockwell and Bettina Berendt<br/><br/>2.1 Busa and the Digital Text 30<br/><br/>2.2 Thesaurus Linguae Graecae and the Ibycus Computer as Infrastructure 32<br/><br/>2.2.1 Complete Data Sets 33<br/><br/>2.3 Cooking with Statistics 35<br/><br/>2.4 Conclusions 37<br/><br/>References 38<br/><br/>Chapter 3 Finding Predictors in Higher Education41<br/>David Eubanks, William Evers Jr., and Nancy Smith<br/><br/>3.1 Contrasting Traditional and Computational Methods 42<br/><br/>3.2 Predictors and Data Exploration 45<br/><br/>3.3 Data Mining Application: An Example 50<br/><br/>3.4 Conclusions 52<br/><br/>References 53<br/><br/>Chapter 4 Educational Data Mining: A MOOC Experience55<br/>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<br/><br/>4.1 Big Data in Education: The Course 55<br/><br/>4.1.1 Iteration 1: Coursera 55<br/><br/>4.1.2 Iteration 2: edX 56<br/><br/>4.2 Cognitive Tutor Authoring Tools 57<br/><br/>4.3 Bazaar 58<br/><br/>4.4 Walkthrough 58<br/><br/>4.4.1 Course Content 58<br/><br/>4.4.2 Research on BDEMOOC 61<br/><br/>4.5 Conclusion 65<br/><br/>Acknowledgments 65<br/><br/>References 65<br/><br/>Chapter 5 Data Mining and Action Research 67<br/>Ellina Chernobilsky, Edith Ries, and Joanne Jasmine<br/><br/>5.1 Process 69<br/><br/>5.2 Design Methodology 71<br/><br/>5.3 Analysis and Interpretation of Data 72<br/><br/>5.3.1 Quantitative Data Analysis and Interpretation 73<br/><br/>5.3.2 Qualitative Data Analysis and Interpretation 74<br/><br/>5.4 Challenges 75<br/><br/>5.5 Ethics 76<br/><br/>5.6 Role of Administration in the Data Collection Process 76<br/><br/>5.7 Conclusion 77<br/><br/>References 77<br/><br/>Part II Pedagogical Applications of EDM79<br/><br/>Chapter 6 Design of an Adaptive Learning System and Educational Data Mining81<br/>Zhiyong Liu and Nick Cercone<br/><br/>6.1 Dimensionalities of the User Model in ALS 83<br/><br/>6.2 Collecting Data for ALS 85<br/><br/>6.3 Data Mining in ALS 86<br/><br/>6.3.1 Data Mining for User Modeling 87<br/><br/>6.3.2 Data Mining for Knowledge Discovery 88<br/><br/>6.4 ALS Model and Function Analyzing 90<br/><br/>6.4.1 Introduction of Module Functions 90<br/><br/>6.4.2 Analyzing the Workflow 93<br/><br/>6.5 Future Works 94<br/><br/>6.6 Conclusions 94<br/><br/>Acknowledgment 95<br/><br/>References 95<br/><br/>Chapter 7 The “Geometry” of Naive Bayes: Teaching Probabilities by “Drawing” Them99<br/>Giorgio Maria Di Nunzio<br/><br/>7.1 Introduction 99<br/><br/>7.1.1 Main Contribution 100<br/><br/>7.1.2 Related Works 101<br/><br/>7.2 The Geometry of NB Classification 102<br/><br/>7.2.1 Mathematical Notation 102<br/><br/>7.2.2 Bayesian Decision Theory 103<br/><br/>7.3 Two-Dimensional Probabilities 105<br/><br/>7.3.1 Working with Likelihoods and Priors Only 107<br/><br/>7.3.2 De‐normalizing Probabilities 108<br/><br/>7.3.3 NB Approach 109<br/><br/>7.3.4 Bernoulli Naïve Bayes 110<br/><br/>7.4 A New Decision Line: Far from the Origin 111<br/><br/>7.4.1 De‐normalization Makes (Some) Problems Linearly Separable 112<br/><br/>7.5 Likelihood Spaces, When Logarithms make a Difference (or a SUM) 114<br/><br/>7.5.1 De‐normalization Makes (Some) Problems Linearly Separable 115<br/><br/>7.5.2 A New Decision in Likelihood Spaces 116<br/><br/>7.5.3 A Real Case Scenario: Text Categorization 117<br/><br/>7.6 Final Remarks 118<br/><br/>References 119<br/><br/>Chapter 8 Examining the Learning Networks of a MOOC121<br/>Meaghan Brugha and Jean‐Paul Restoule<br/><br/>8.1 Review of Literature 122<br/><br/>8.2 Course Context 124<br/><br/>8.3 Results and Discussion 125<br/><br/>8.4 Recommendations for Future Research 133<br/><br/>8.5 Conclusions 134<br/><br/>References 135<br/><br/>Chapter 9 Exploring the Usefulness of Adaptive ELearning Laboratory Environments in Teaching Medical Science139<br/>Thuan Thai and Patsie Polly<br/><br/>9.1 Introduction 139<br/><br/>9.2 Software for Learning and Teaching 141<br/><br/>9.2.1 Reflective Practice: ePortfolio 141<br/><br/>9.2.2 Online Quizzes 143<br/><br/>9.2.3 Online Practical Lessons 144<br/><br/>9.2.4 Virtual Laboratories 145<br/><br/>9.2.5 The Gene Suite 147<br/><br/>9.3 Potential Limitations 152<br/><br/>9.4 Conclusion 153<br/><br/>Acknowledgments 153<br/><br/>References 154<br/><br/>Chapter 10 Investigating Co‐Occurrence Patterns of Learners’ Grammatical Errors across Proficiency Levels and Essay Topics Based on Association Analysis 157<br/>Yutaka Ishii<br/><br/>10.1 Introduction 157<br/><br/>10.1.1 The Relationship between Data Mining and Educational Research 157<br/><br/>10.1.2 English Writing Instruction in the Japanese Context 158<br/><br/>10.2 Literature Review 159<br/><br/>10.3 Method 160<br/><br/>10.3.1 Konan‐JIEM Learner Corpus 160<br/><br/>10.3.2 Association Analysis 162<br/><br/>10.4 Experiment 1 162<br/><br/>10.5 Experiment 2 163<br/><br/>10.6 Discussion and Conclusion 164<br/><br/>Appendix A: Example of Learner’s Essay (University Life) 164<br/><br/>Appendix B: Support Values of all Topics 165<br/><br/>Appendix C: Support Values of Advanced, Intermediate, and Beginner Levels of Learners 168<br/><br/>References 169<br/><br/>Part III EDM and Educational Research 173<br/><br/>Chapter 11 Mining Learning Sequences in MOOCs: Does Course Design Constrain Students’ Behaviors Or Do Students Shape Their Own Learning? 175<br/>Lorenzo Vigentini, Simon McIntyre, Negin Mirriahi, and Dennis Alonzo<br/><br/>11.1 Introduction 175<br/><br/>11.1.1 Perceptions and Challenges of MOOC Design 176<br/><br/>11.1.2 What Do We Know About Participants’ Navigation: Choice and Control 177<br/><br/>11.2 Data Mining in MOOCs: Related Work 178<br/><br/>11.2.1 Setting the Hypotheses 179<br/><br/>11.3 The Design and Intent of the LTTO MOOC 180<br/><br/>11.3.1 Course Grading and Certification 183<br/><br/>11.3.2 Delivering the Course 183<br/><br/>11.3.3 Operationalize Engagement, Personal Success, and Course Success in LTTO 184<br/><br/>11.4 Data Analysis 184<br/><br/>11.4.1 Approaches to Process the Data Sources 185<br/><br/>11.4.2 LTTO in Numbers 186<br/><br/>11.4.3 Characterizing Patterns of Completion and Achievement 186<br/><br/>11.4.4 Redefining Participation and Engagement 189<br/><br/>11.5 Mining Behaviors and Intents 191<br/><br/>11.5.1 Participants’ Intent and Behaviors: A Classification Model 191<br/><br/>11.5.2 Natural Clustering Based on Behaviors 194<br/><br/>11.5.3 Stated Intents and Behaviors: Are They Related? 198<br/><br/>11.6 Closing the Loop: Informing Pedagogy and Course Enhancement 198<br/><br/>11.6.1 Conclusions, Lessons Learnt, and Future Directions 200<br/><br/>References 201<br/><br/>Chapter 12 Understanding Communication Patterns in MOOCs: Combining Data Mining and Qualitative Methods 207<br/>Rebecca Eynon, Isis Hjorth, Taha Yasseri, and Nabeel Gillani<br/><br/>12.1 Introduction 207<br/><br/>12.2 Methodological Approaches to Understanding Communication Patterns in MOOCs 209<br/><br/>12.3 Description 210<br/><br/>12.3.1 Structural Connections 211<br/><br/>12.4 Examining Dialogue 213<br/><br/>12.5 Interpretative Models 214<br/><br/>12.6 Understanding Experience 215<br/><br/>12.7 Experimentation 216<br/><br/>12.8 Future Research 217<br/><br/>References 218<br/><br/>Chapter 13 An Example of Data Mining: Exploring The Relationship Between Applicant Attributes and Academic Measures of Success in a Pharmacy Program 223<br/>Dion Brocks and Ken Cor<br/><br/>13.1 Introduction 223<br/><br/>13.2 Methods 225<br/><br/>13.3 Results 228<br/><br/>13.4 Discussion 230<br/><br/>13.4.1 Prerequisite Predictors 230<br/><br/>13.4.2 Demographic Predictors 232<br/><br/>13.5 Conclusion 234<br/><br/>Appendix A 234<br/><br/>References 236<br/><br/>Chapter 14 A New Way of Seeing: Using a Data Mining Approach to Understand Children’s Views of Diversity and “Difference” in Picture Books237<br/>Robin A. Moeller and Hsin‐liang Chen<br/><br/>14.1 Introduction 237<br/><br/>14.2 Study 1: Using Data Mining to Better Understand Perceptions of Race 238<br/><br/>14.2.1 Background 238<br/><br/>14.2.2 Research Questions 239<br/><br/>14.2.3 Methods 240<br/><br/>14.2.4 Findings 240<br/><br/>14.2.5 Discussion 248<br/><br/>14.3 Study 2: Translating Data Mining Results to Picture Book Concepts of “Difference” 248<br/><br/>14.3.1 Background 248<br/><br/>14.3.2 Research Questions 249<br/><br/>14.3.3 Methodology 250<br/><br/>14.3.4 Findings 250<br/><br/>14.3.5 Discussion and Implications 252<br/><br/>14.4 Conclusions 252<br/><br/>References 252<br/><br/>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 Practices255<br/>Alison L. Bailey, Anne Blackstock‐Bernstein, Eve Ryan, and Despina Pitsoulakis<br/><br/>15.1 Introduction 255<br/><br/>15.2 Identifying the Problem 256<br/><br/>15.3 Use of Corpora and Technology in Language Instruction and Assessment 261<br/><br/>15.3.1 Language Corpora in ESL and EFL Teaching and Learning 261<br/><br/>15.3.2 Previous Extensions of Corpus Linguistics to School‐Age Language 262<br/><br/>15.3.3 Corpus Linguistics in Language Assessment 263<br/><br/>15.3.4 Big Data Purposes, Techniques, and Technology 264<br/><br/>15.4 Creating a School‐Age Learner Corpus and Digital Data Analytics System 266<br/><br/>15.4.1 Language Measures Included in DRGON 267<br/><br/>15.4.2 The DLLP as a Promising Practice 268<br/><br/>15.5 Next Steps, “Modest Data,” and Closing Remarks 269<br/><br/>Acknowledgments 271<br/><br/>Appendix A: Examples of Oral and Written Explanation Elicitation Prompts 272<br/><br/>References 272<br/><br/>Index 277 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning <br/><br/>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.<br/><br/> Includes case studies where data mining techniques have been effectively applied to advance teaching and learning<br/>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<br/>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<br/>Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics<br/>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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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Education |
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Research |
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Statistical methods. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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Educational statistics |
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Data processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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Data mining. |
| 653 ## - INDEX TERM--UNCONTROLLED |
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Learning analytics. |
| 655 ## - INDEX TERM--GENRE/FORM |
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Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
ElAtia, Samira, |
| Dates associated with a name |
1973- |
| Relator term |
editor. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Ipperciel, Donald, |
| Dates associated with a name |
1967- |
| Relator term |
editor. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Zaïane, Osmar, |
| Relator term |
editor. |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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Full text available at Wiley Online Library Click here to view |
| Uniform Resource Identifier |
https://onlinelibrary.wiley.com/doi/book/10.1002/9781118998205 |
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