Handbook of Educational Data Mining | Cristobal Romero, Sebastian Vent

Book

Handbook of Educational Data Mining

ABSTRACT

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

chapter 1|6 pages

Introduction

part |2 pages

Part I Basic Techniques, Surveys and Tutorials

chapter 2|18 pages

Visualization in Educational Environments

chapter 3|16 pages

Basics of Statistical Analysis of Interactions Data from Web-Based Learning Environments

chapter 4|14 pages

A Data Repository for the EDM Community: The PSLC DataShop

chapter 5|18 pages

Classifiers for Educational Data Mining

chapter 6|18 pages

Clustering Educational Data

chapter 7|14 pages

Association Rule Mining in Learning Management Systems

chapter 8|16 pages

Sequential Pattern Analysis of Learning Logs: Methodology and Applications

chapter 9|20 pages

Process Mining from Educational Data

chapter 10|14 pages

Modeling Hierarchy and Dependence among Task Responses in Educational Data Mining

part |2 pages

Part II Case Studies

chapter 11|14 pages

Novel Derivation and Application of Skill Matrices: The q-Matrix Method

chapter 12|14 pages

Educational Data Mining to Support Group Work in Software Development Projects

chapter 13|14 pages

Multi-Instance Learning versus Single-Instance Learning for Predicting the Student’s Performance

chapter 14|12 pages

A Response-Time Model for Bottom-Out Hints as Worked Examples

chapter 15|18 pages

Automatic Recognition of Learner Types in Exploratory Learning Environments

chapter 16|14 pages

Modeling Affect by Mining Students’ Interactions within Learning Environments

chapter 17|12 pages

Measuring Correlation of Strong Symmetric Association Rules in Educational Data

chapter 18|16 pages

Data Mining for Contextual Educational Recommendation and Evaluation Strategies

chapter 19|14 pages

Link Recommendation in E-Learning Systems Based on Content-Based Student Proles

chapter 20|12 pages

Log-Based Assessment of Motivation in Online Learning

chapter 21|12 pages

Mining Student Discussions for Profiling Participation and Scaffolding Learning

chapter 22|12 pages

Analysis of Log Data from a Web-Based Learning Environment: A Case Study

chapter 23|16 pages

Bayesian Networks and Linear Regression Models of Students’ Goals, Moods, and Emotions

chapter 24|14 pages

Capturing and Analyzing Student Behavior in a Virtual Learning Environment: A Case Study on Usage of Library Resources

chapter 25|13 pages

Anticipating Students’ Failure As Soon As Possible

chapter 26|12 pages

Using Decision Trees for Improving AEH Courses

chapter 27|12 pages

Validation Issues in Educational Data Mining: The Case of HTML-Tutor and iHelp

chapter 28|28 pages

Lessons from Project LISTEN’s Session Browser

chapter 29|10 pages

Using Fine-Grained Skill Models to Fit Student Performance with Bayesian Networks

chapter 30|14 pages

Mining for Patterns of Incorrect Response in Diagnostic Assessment Data

chapter 31|10 pages

Machine-Learning Assessment of Students’ Behavior within Interactive Learning Environments

chapter 32|16 pages

Learning Procedural Knowledge from User Solutions to Ill-Defined Tasks in a Simulated Robotic Manipulator

chapter 33|14 pages

Using Markov Decision Processes for Automatic Hint Generation

chapter 34|12 pages

Data Mining Learning Objects

chapter 35|12 pages

An Adaptive Bayesian Student Model for Discovering the Student’s Learning Style and Preferences