Data mining and learning analytics: applications in educational research

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.