Data Mining and Learning Analytics: Applications in Educational Research (Wiley Series on Methods and Applications in Data Mining)

Synopsis

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.--publisher's Description. Title Page ; Copyright Page ; Contents; Notes On Contributors; Introduction: Education At Computational Crossroads; Part I At The Intersection Of Two Fields: Edm ; Chapter 1 Educational Process Mining: A Tutorial And Case€study Using Moodle Data Sets; 1.1 Background; 1.2 Data Description And€preparation; 1.2.1 Preprocessing Log Data; 1.2.2 Clustering Approach For€grouping Log Data; 1.3 Working With€prom; 1.3.1 Discovered Models; 1.3.2 Analysis Of€the€models' Performance; 1.4 Conclusion; Acknowledgments; References; Chapter 2 On Big Data And€text Mining In€the€humanities; 2.1 Busa And€the€digital Text2.2 Thesaurus Linguae Graecae And€the€ibycus Computer As€infrastructure; 2.2.1 Complete Data Sets; 2.3 Cooking With€statistics; 2.4 Conclusions; References. Chapter 3 Finding Predictors In€higher Education; 3.1 Contrasting Traditional And Computational Methods; 3.2 Predictors And€data Exploration; 3.3 Data Mining Application: An€example; 3.4 Conclusions; References; Chapter 4 Educational Data Mining: A€mooc Experience; 4.1 Big Data In€education: The€course; 4.1.1 Iteration 1: Coursera; 4.1.2 Iteration 2: Edx; 4.2 Cognitive Tutor Authoring Tools; 4.3 Bazaar; 4.4 Walkthrough4.4.1 Course Content; 4.4.2 Research On€bdemooc; 4.5 Conclusion; Acknowledgments; References. Chapter 5 Data Mining And Action Research; 5.1 Process; 5.2 Design Methodology; 5.3 Analysis And€interpretation Of€data; 5.3.1 Quantitative Data Analysis And€interpretation; 5.3.2 Qualitative Data Analysis And€interpretation; 5.4 Challenges; 5.5 Ethics; 5.6 Role Of€administration In€the€data Collection Process; 5.7 Conclusion; References; Part Ii Pedagogical Applications Of Edm ; Chapter 6 Design Of€an€adaptive Learning System And€educational Data€mining; 6.1 Dimensionalities Of€the€user Model In€als6.2 Collecting Data For€als; 6.3 Data Mining In€als; 6.3.1 Data Mining For€user Modeling; 6.3.2 Data Mining For€knowledge Discovery; 6.4 Als Model And€function Analyzing; 6.4.1 Introduction Of€module Functions; 6.4.2 Analyzing The€workflow; 6.5 Future Works; 6.6 Conclusions; Acknowledgment; References. Chapter 7 The Geometry Of NaÏve€bayes: Teaching Probabilities By Drawing€them; 7.1 Introduction; 7.1.1 Main Contribution; 7.1.2 Related Works; 7.2 The Geometry Of€nb Classification; 7.2.1 Mathematical Notation; 7.2.2 Bayesian Decision Theory; 7.3 Two-dimensional Probabilities7.3.1 Working With€likelihoods And€priors Only; 7.3.2 De-normalizing Probabilities ; 7.3.3 Nb Approach; 7.3.4 Bernoulli Naïve Bayes; 7.4 A New Decision Line: Far From€the€origin; 7.4.1 De-normalization Makes (some) Problems Linearly Separable ; 7.5 Likelihood Spaces, When Logarithms Make A€difference (or A€sum); 7.5.1 De-normalization Makes (some) Problems Linearly Separable ; 7.5.2 A New Decision In€likelihood Spaces; 7.5.3 A Real Case Scenario: Text Categorization; 7.6 Final Remarks; References; Chapter 8 Examining The€learning Networks Of€a€mooc; 8.1 Review Of€literature. Edited By Samira Elatia, Donald Ipperciel, Osmar R. Zaiane. Includes Bibliographical References And Index.