Data mining and learning analytics : applications in educational research / [edited by] Samira ElAtia, Donald Ipperciel and Osmar R. Zaiane.

Includes bibliographical references and index.

PART I AT THE INTERSECTION OF TWO FIELDS: EDM
CHAPTER 1 EDUCATIONAL PROCESS MINING: A TUTORIAL AND CASE STUDY USING MOODLE DATA SETS --
CHAPTER 2 ON BIG DATA AND TEXT MINING IN THE HUMANITIES --
CHAPTER 3 FINDING PREDICTORS IN HIGHER EDUCATION --
CHAPTER 4 EDUCATIONAL DATA MINING: A MOOC EXPERIENCE --
CHAPTER 5 DATA MINING AND ACTION RESEARCH --
PART II PEDAGOGICAL APPLICATIONS OF EDM
CHAPTER 6 DESIGN OF AN ADAPTIVE LEARNING SYSTEM AND EDUCATIONAL DATA MINING --
CHAPTER 7 THE GEOMETRY OF NAIVE BAYES: TEACHING PROBABILITIES BY DRAWING THEM --
CHAPTER 8 EXAMINING THE LEARNING NETWORKS OF A MOOC --
CHAPTER 9 EXPLORING THE USEFULNESS OF ADAPTIVE ELEARNING LABORATORY ENVIRONMENTS IN TEACHING MEDICAL SCIENCE --
CHAPTER 10 INVESTIGATING CO -OCCURRENCE PATTERNS OF LEARNERS GRAMMATICAL ERRORS ACROSS PROFICIENCY LEVELS AND ESSAY TOPICS BASED ON ASSOCIATION ANALYSIS --
PART III EDM AND EDUCATIONAL RESEARCH
CHAPTER 11 MINING LEARNING SEQUENCES IN MOOCs: DOES COURSE DESIGN CONSTRAIN STUDENTS BEHAVIORS OR DO STUDENTS SHAPE THEIR OWN LEARNING?
CHAPTER 12 UNDERSTANDING COMMUNICATION PATTERNS IN MOOCs: COMBINING DATA MINING AND QUALITATIVE METHODS --
CHAPTER 13 AN EXAMPLE OF DATA MINING: EXPLORING THE RELATIONSHIP BETWEEN APPLICANT ATTRIBUTES AND ACADEMIC MEASURES OF SUCCESS IN A PHARMACY PROGRAM --
CHAPTER 14 A NEW WAY OF SEEING: USING A DATA MINING APPROACH TO UNDERSTAND CHILDREN S VIEWS OF DIVERSITY AND DIFFERENCE IN PICTURE BOOKS --
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.

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.