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Data Mining and Learning Analytics

Book Description

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.

Table of Contents

  1. COVER
  2. TITLE PAGE
  3. NOTES ON CONTRIBUTORS
  4. INTRODUCTION
    1. I.1 PART I: AT THE INTERSECTION OF TWO FIELDS: EDM
    2. I.2 PART II: PEDAGOGICAL APPLICATIONS OF EDM
    3. I.3 PART III: EDM AND EDUCATIONAL RESEARCH
    4. REFERENCES
  5. PART I: AT THE INTERSECTION OF TWO FIELDS: EDM
    1. CHAPTER 1: EDUCATIONAL PROCESS MINING
      1. 1.1 BACKGROUND
      2. 1.2 DATA DESCRIPTION AND PREPARATION
      3. 1.3 WORKING WITH ProM
      4. 1.4 CONCLUSION
      5. ACKNOWLEDGMENTS
      6. REFERENCES
    2. CHAPTER 2: ON BIG DATA AND TEXT MINING IN THE HUMANITIES
      1. 2.1 BUSA AND THE DIGITAL TEXT
      2. 2.2 THESAURUS LINGUAE GRAECAE AND THE IBYCUS COMPUTER AS INFRASTRUCTURE
      3. 2.3 COOKING WITH STATISTICS
      4. 2.4 CONCLUSIONS
      5. REFERENCES
    3. CHAPTER 3: FINDING PREDICTORS IN HIGHER EDUCATION
      1. 3.1 CONTRASTING TRADITIONAL AND COMPUTATIONAL METHODS
      2. 3.2 PREDICTORS AND DATA EXPLORATION
      3. 3.3 DATA MINING APPLICATION: AN EXAMPLE
      4. 3.4 CONCLUSIONS
      5. REFERENCES
    4. CHAPTER 4: EDUCATIONAL DATA MINING
      1. 4.1 BIG DATA IN EDUCATION: THE COURSE
      2. 4.2 COGNITIVE TUTOR AUTHORING TOOLS
      3. 4.3 BAZAAR
      4. 4.4 WALKTHROUGH
      5. 4.5 CONCLUSION
      6. ACKNOWLEDGMENTS
      7. REFERENCES
    5. CHAPTER 5: DATA MINING AND ACTION RESEARCH
      1. 5.1 PROCESS
      2. 5.2 DESIGN METHODOLOGY
      3. 5.3 ANALYSIS AND INTERPRETATION OF DATA
      4. 5.4 CHALLENGES
      5. 5.5 ETHICS
      6. 5.6 ROLE OF ADMINISTRATION IN THE DATA COLLECTION PROCESS
      7. 5.7 CONCLUSION
      8. REFERENCES
  6. PART II: PEDAGOGICAL APPLICATIONS OF EDM
    1. CHAPTER 6: DESIGN OF AN ADAPTIVE LEARNING SYSTEM AND EDUCATIONAL DATA MINING
      1. 6.1 DIMENSIONALITIES OF THE USER MODEL IN ALS
      2. 6.2 COLLECTING DATA FOR ALS
      3. 6.3 DATA MINING IN ALS
      4. 6.4 ALS MODEL AND FUNCTION ANALYZING
      5. 6.5 FUTURE WORKS
      6. 6.6 CONCLUSIONS
      7. ACKNOWLEDGMENT
      8. REFERENCES
    2. CHAPTER 7: THE “GEOMETRY” OF NAÏVE BAYES
      1. 7.1 INTRODUCTION
      2. 7.2 THE GEOMETRY OF NB CLASSIFICATION
      3. 7.3 TWO‐DIMENSIONAL PROBABILITIES
      4. 7.4 A NEW DECISION LINE: FAR FROM THE ORIGIN
      5. 7.5 LIKELIHOOD SPACES, WHEN LOGARITHMS MAKE A DIFFERENCE (OR A SUM)
      6. 7.6 FINAL REMARKS
      7. REFERENCES
    3. CHAPTER 8: EXAMINING THE LEARNING NETWORKS OF A MOOC
      1. 8.1 REVIEW OF LITERATURE
      2. 8.2 COURSE CONTEXT
      3. 8.3 RESULTS AND DISCUSSION
      4. 8.4 RECOMMENDATIONS FOR FUTURE RESEARCH
      5. 8.5 CONCLUSIONS
      6. REFERENCES
    4. CHAPTER 9: EXPLORING THE USEFULNESS OF ADAPTIVE ELEARNING LABORATORY ENVIRONMENTS IN TEACHING MEDICAL SCIENCE
      1. 9.1 INTRODUCTION
      2. 9.2 SOFTWARE FOR LEARNING AND TEACHING
      3. 9.3 POTENTIAL LIMITATIONS
      4. 9.4 CONCLUSION
      5. ACKNOWLEDGMENTS
      6. REFERENCES
    5. CHAPTER 10: INVESTIGATING CO‐OCCURRENCE PATTERNS OF LEARNERS’ GRAMMATICAL ERRORS ACROSS PROFICIENCY LEVELS AND ESSAY TOPICS BASED ON ASSOCIATION ANALYSIS
      1. 10.1 INTRODUCTION
      2. 10.2 LITERATURE REVIEW
      3. 10.3 METHOD
      4. 10.4 EXPERIMENT 1
      5. 10.5 EXPERIMENT 2
      6. 10.6 DISCUSSION AND CONCLUSION
      7. APPENDIX A: EXAMPLE OF LEARNER’S ESSAY (UNIVERSITY LIFE)
      8. APPENDIX B: SUPPORT VALUES OF ALL TOPICS
      9. APPENDIX C: SUPPORT VALUES OF ADVANCED, INTERMEDIATE, AND BEGINNER LEVELS OF LEARNERS
      10. REFERENCES
  7. PART III: EDM AND EDUCATIONAL RESEARCH
    1. CHAPTER 11: MINING LEARNING SEQUENCES IN MOOCs
      1. 11.1 INTRODUCTION
      2. 11.2 DATA MINING IN MOOCs: RELATED WORK
      3. 11.3 THE DESIGN AND INTENT OF THE LTTO MOOC
      4. 11.4 DATA ANALYSIS
      5. 11.5 MINING BEHAVIORS AND INTENTS
      6. 11.6 CLOSING THE LOOP: INFORMING PEDAGOGY AND COURSE ENHANCEMENT
      7. REFERENCES
    2. CHAPTER 12: UNDERSTANDING COMMUNICATION PATTERNS IN MOOCs
      1. 12.1 INTRODUCTION
      2. 12.2 METHODOLOGICAL APPROACHES TO UNDERSTANDING COMMUNICATION PATTERNS IN MOOCs
      3. 12.3 DESCRIPTION
      4. 12.4 EXAMINING DIALOGUE
      5. 12.5 INTERPRETATIVE MODELS
      6. 12.6 UNDERSTANDING EXPERIENCE
      7. 12.7 EXPERIMENTATION
      8. 12.8 FUTURE RESEARCH
      9. REFERENCES
    3. CHAPTER 13: AN EXAMPLE OF DATA MINING
      1. 13.1 INTRODUCTION
      2. 13.2 METHODS
      3. 13.3 RESULTS
      4. 13.4 DISCUSSION
      5. 13.5 CONCLUSION
      6. APPENDIX A
      7. REFERENCES
    4. CHAPTER 14: A NEW WAY OF SEEING
      1. 14.1 INTRODUCTION
      2. 14.2 STUDY 1: USING DATA MINING TO BETTER UNDERSTAND PERCEPTIONS OF RACE
      3. 14.3 STUDY 2: TRANSLATING DATA MINING RESULTS TO PICTURE BOOK CONCEPTS OF “DIFFERENCE”
      4. 14.4 CONCLUSIONS
      5. REFERENCES
    5. CHAPTER 15: DATA MINING WITH NATURAL LANGUAGE PROCESSING AND CORPUS LINGUISTICS
      1. 15.1 INTRODUCTION
      2. 15.2 IDENTIFYING THE PROBLEM
      3. 15.3 USE OF CORPORA AND TECHNOLOGY IN LANGUAGE INSTRUCTION AND ASSESSMENT
      4. 15.4 CREATING A SCHOOL‐AGE LEARNER CORPUS AND DIGITAL DATA ANALYTICS SYSTEM
      5. 15.5 NEXT STEPS, “MODEST DATA,” AND CLOSING REMARKS
      6. ACKNOWLEDGMENTS
      7. APPENDIX A  EXAMPLES OF ORAL AND WRITTEN EXPLANATION ELICITATION PROMPTS
      8. REFERENCES
  8. INDEX
  9. END USER LICENSE AGREEMENT