Handbook of Educational Data Mining

Cristobal Romero, Sebastian Ventura, Mykola Pechenizkiy, Ryan S.J.d. Baker, Cristobal Romero, Sebastian Ventura, Mykola Pechenizkiy, Ryan S.J.d. Baker

• Publisher: CRC Press
• Year: 2010
• Print ISBN: 9781439804575
• eBook ISBN: 9781439804582
• Edition: 1
• Topic: Economics
• Subtopic: Statistics for Business & Economics
• Index: Economics

Handbook of Educational Data Mining (EDM) provides a thorough overview of the current state of knowledge in this area. The first part of the book includes nine surveys and tutorials on the principal data mining techniques that have been applied in education. The second part presents a set of 25 case studies that give a rich overview of the problems

Table of contents

• Front cover
• Dedication
• Contents
• Preface
• Editors
• Contributors
• Chapter 1. Introduction
• Part I. Basic Techniques, Surveys and Tutorials
• Chapter 2. 2Visualization in Educational Environments
• Chapter 3. 3Basics of Statistical Analysis of Interactions Data from Web-Based Learning Environments
• Chapter 4. A Data Repository for the EDM Community: The PSLC DataShop
• Chapter 5.Classifiers for Educational Data Mining
• Chapter 6. Clustering Educational Data
• Chapter 7. Association Rule Mining in Learning Management Systems
• Chapter 8. Sequential Pattern Analysis of Learning Logs: Methodology and Applications
• Chapter 9. Process Mining from Educational Data
• Chapter 10. Modeling Hierarchy and Dependence among Task Responses in Educational Data Mining
• Part II. Case Studies
• Chapter 11. Novel Derivation and Application of Skill Matrices: The q-Matrix Method
• Chapter 12. Educational Data Mining to Support Group Work in Software Development Projects
• Chapter 13. Multi-Instance Learning versus Single-Instance Learning for Predicting the Student’s Performance
• Chapter 14. A Response-Time Model for Bottom-Out Hints as Worked Examples
• Chapter 15. Automatic Recognition of Learner Types in Exploratory Learning Environments
• Chapter 16. Modeling Affect by Mining Students’ Interactions within Learning Environments
• Chapter 17. Measuring Correlation of Strong Symmetric Association Rules in Educational Data
• Chapter 18. Data Mining for Contextual Educational Recommendation and Evaluation Strategies
• Chapter 19. Link Recommendation in E-Learning Systems Based on Content-Based Student Profiles
• Chapter 20. Log-Based Assessment of Motivation in Online Learning
• Chapter 21. Mining Student Discussions for Profiling Participation and Scaffolding Learning
• Chapter 22. Analysis of Log Data from a Web-Based Learning Environment: A Case Study
• Chapter 23. Bayesian Networks and Linear Regression Models of Students’ Goals, Moods, and Emotions
• Chapter 24. Capturing and Analyzing Student Behavior in a Virtual Learning Environment: A Case Study on Usage of Library Resources
• Chapter 25. Anticipating Students’ Failure As Soon As Possible
• Chapter 26. Using Decision Trees for Improving AEH Courses
• Chapter 27. Validation Issues in Educational Data Mining: The Case of HTML-Tutor and iHelp
• Chapter 28. Lessons from Project LISTEN’s Session Browser
• Chapter 29. Using Fine-Grained Skill Models to Fit Student Performance with Bayesian Networks
• Chapter 30. Mining for Patterns of Incorrect Response in Diagnostic Assessment Data
• Chapter 31. Machine-Learning Assessment of Students’ Behavior within Interactive Learning Environments
• Chapter 32. Learning Procedural Knowledge from User Solutions to Ill-Defined Tasks in a Simulated Robotic Manipulator
• Chapter 33. Using Markov Decision Processes for Automatic Hint Generation
• Chapter 34. Data Mining Learning Objects
• Chapter 35. An Adaptive Bayesian Student Model for Discovering the Student’s Learning Style and Preferences
• Back cover