Data Mining and Learning Analytics Applications in Educational Research 1st Edition by Samira Elatia, Donald Ipperciel, Osmar Zaiane 1118998212 9781118998212

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ISBN-10 :  1118998212 

ISBN-13 :  9781118998212

Author: Samira Elatia, Donald Ipperciel, Osmar R. Zaiane 

Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning  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.  Includes case studies where data mining techniques have been effectively applied to advance teaching and learning 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 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 Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics 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.

Data Mining and Learning Analytics Applications in Educational Research 1st Table of contents:

I.1 PART I: AT THE INTERSECTION OF TWO FIELDS: EDM
I.2 PART II: PEDAGOGICAL APPLICATIONS OF EDM
I.3 PART III: EDM AND EDUCATIONAL RESEARCH
REFERENCES
PART I: AT THE INTERSECTION OF TWO FIELDS: EDM
CHAPTER 1: EDUCATIONAL PROCESS MINING
1.1 BACKGROUND
1.2 DATA DESCRIPTION AND PREPARATION
1.3 WORKING WITH ProM
1.4 CONCLUSION
ACKNOWLEDGMENTS
REFERENCES
CHAPTER 2: ON BIG DATA AND TEXT MINING IN THE HUMANITIES
2.1 BUSA AND THE DIGITAL TEXT
2.2 THESAURUS LINGUAE GRAECAE AND THE IBYCUS COMPUTER AS INFRASTRUCTURE
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
4.1 BIG DATA IN EDUCATION: THE COURSE
4.2 COGNITIVE TUTOR AUTHORING TOOLS
4.3 BAZAAR
4.4 WALKTHROUGH
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
5.4 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 ALS
6.2 COLLECTING DATA FOR ALS
6.3 DATA MINING IN ALS
6.4 ALS MODEL AND FUNCTION ANALYZING
6.5 FUTURE WORKS
6.6 CONCLUSIONS
ACKNOWLEDGMENT
REFERENCES
CHAPTER 7: THE “GEOMETRY” OF NAÏVE BAYES
7.1 INTRODUCTION
7.2 THE GEOMETRY OF NB CLASSIFICATION
7.3 TWO‐DIMENSIONAL PROBABILITIES
7.4 A NEW DECISION LINE: FAR FROM THE ORIGIN
7.5 LIKELIHOOD SPACES, WHEN LOGARITHMS MAKE A DIFFERENCE (OR A SUM)
7.6 FINAL REMARKS
REFERENCES
CHAPTER 8: EXAMINING THE LEARNING NETWORKS OF A MOOC
8.1 REVIEW OF LITERATURE
8.2 COURSE CONTEXT
8.3 RESULTS AND DISCUSSION
8.4 RECOMMENDATIONS FOR FUTURE RESEARCH
8.5 CONCLUSIONS
REFERENCES
CHAPTER 9: EXPLORING THE USEFULNESS OF ADAPTIVE ELEARNING LABORATORY ENVIRONMENTS IN TEACHING MEDICAL SCIENCE
9.1 INTRODUCTION
9.2 SOFTWARE FOR LEARNING AND TEACHING
9.3 POTENTIAL LIMITATIONS
9.4 CONCLUSION
ACKNOWLEDGMENTS
REFERENCES
CHAPTER 10: INVESTIGATING CO‐OCCURRENCE PATTERNS OF LEARNERS’ GRAMMATICAL ERRORS ACROSS PROFICIENCY LEVELS AND ESSAY TOPICS BASED ON ASSOCIATION ANALYSIS
10.1 INTRODUCTION
10.2 LITERATURE REVIEW
10.3 METHOD
10.4 EXPERIMENT 1
10.5 EXPERIMENT 2
10.6 DISCUSSION AND CONCLUSION
APPENDIX A: EXAMPLE OF LEARNER’S ESSAY (UNIVERSITY LIFE)
APPENDIX B: SUPPORT VALUES OF ALL TOPICS
APPENDIX C: SUPPORT VALUES OF ADVANCED, INTERMEDIATE, AND BEGINNER LEVELS OF LEARNERS
REFERENCES
PART III: EDM AND EDUCATIONAL RESEARCH
CHAPTER 11: MINING LEARNING SEQUENCES IN MOOCs
11.1 INTRODUCTION
11.2 DATA MINING IN MOOCs: RELATED WORK
11.3 THE DESIGN AND INTENT OF THE LTTO MOOC
11.4 DATA ANALYSIS
11.5 MINING BEHAVIORS AND INTENTS
11.6 CLOSING THE LOOP: INFORMING PEDAGOGY AND COURSE ENHANCEMENT
REFERENCES
CHAPTER 12: UNDERSTANDING COMMUNICATION PATTERNS IN MOOCs
12.1 INTRODUCTION
12.2 METHODOLOGICAL APPROACHES TO UNDERSTANDING COMMUNICATION PATTERNS IN MOOCs
12.3 DESCRIPTION
12.4 EXAMINING DIALOGUE
12.5 INTERPRETATIVE MODELS
12.6 UNDERSTANDING EXPERIENCE
12.7 EXPERIMENTATION
12.8 FUTURE RESEARCH
REFERENCES
CHAPTER 13: AN EXAMPLE OF DATA MINING
13.1 INTRODUCTION
13.2 METHODS
13.3 RESULTS
13.4 DISCUSSION
13.5 CONCLUSION
APPENDIX A
REFERENCES
CHAPTER 14: A NEW WAY OF SEEING
14.1 INTRODUCTION
14.2 STUDY 1: USING DATA MINING TO BETTER UNDERSTAND PERCEPTIONS OF RACE
14.3 STUDY 2: TRANSLATING DATA MINING RESULTS TO PICTURE BOOK CONCEPTS OF “DIFFERENCE”
14.4 CONCLUSIONS
REFERENCES
CHAPTER 15: DATA MINING WITH NATURAL LANGUAGE PROCESSING AND CORPUS LINGUISTICS
15.1 INTRODUCTION
15.2 IDENTIFYING THE PROBLEM
15.3 USE OF CORPORA AND TECHNOLOGY IN LANGUAGE INSTRUCTION AND ASSESSMENT
15.4 CREATING A SCHOOL‐AGE LEARNER CORPUS AND DIGITAL DATA ANALYTICS SYSTEM
15.5 NEXT STEPS, “MODEST DATA,” AND CLOSING REMARKS

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