Handbook of Educational Data Mining

Name: Handbook of Educational Data Mining
Brand: Taylor & Francis Inc
Price: 222.9 AUD
Availability: BackOrder

By: Cristobal Romero (Editor), Sebastian Ventura (Editor), Mykola Pechenizkiy (Editor), Ryan S.J.d. Baker (Editor)

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Hardcover | 25 October 2010 | Edition Number 1

At a Glance

Hardcover
535 Pages

Dimensions(cm)
25.4 x 17.8 x 2.54

Hardcover

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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 that EDM has addressed.

Researchers at the Forefront of the Field Discuss Essential Topics and the Latest Advances
With contributions by well-known researchers from a variety of fields, the book reflects the multidisciplinary nature of the EDM community. It brings the educational and data mining communities together, helping education experts understand what types of questions EDM can address and helping data miners understand what types of questions are important to educational design and educational decision making.

Encouraging readers to integrate EDM into their research and practice, this timely handbook offers a broad, accessible treatment of essential EDM techniques and applications. It provides an excellent first step for newcomers to the EDM community and for active researchers to keep abreast of recent developments in the field.

Industry Reviews

Computer scientists review the current state in using large-scale educational data sets to understand learning better and to provide information about the learning process. ! --SciTech Book News, February 2011

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Preface	p. xi
Editors	p. xv
Contributors	p. xvii
Introduction	p. 1
Basic Techniques, Surveys and Tutorials
Visualization in Educational Environments	p. 9
Basics of Statistical Analysis of Interactions Data from Web-Based Learning Environments	p. 27
A Data Repository for the EDM Community: The PSLC DataShop	p. 43
Classifiers for Educational Data Mining	p. 57
Clustering Educational Data	p. 75
Association Rule Mining in Learning Management Systems	p. 93
Sequential Pattern Analysis of Learning Logs: Methodology and Applications	p. 107
Process Mining from Educational Data	p. 123
Modeling Hierarchy and Dependence among Task Responses in Educational Data Mining	p. 143
Case Studies
Novel Derivation and Application of Skill Matrices: The q-Matrix Method	p. 159
Educational Data Mining to Support Group Work in Software Development Projects	p. 173
Multi-Instance Learning versus Single-Instance Learning for Predicting the Student's Performance	p. 187
A Response-Time Model for Bottom-Out Hints as Worked Examples	p. 201
Automatic Recognition of Learner Types in Exploratory Learning Environments	p. 213
Modeling Affect by Mining Students' Interactions within Learning Environments	p. 231
Measuring Correlation of Strong Symmetric Association Rules in Educational Data	p. 245
Data Mining for Contextual Educational Recommendation and Evaluation Strategies	p. 257
Link Recommendation in E-Learning Systems Based on Content-Based Student Profiles	p. 273
Log-Based Assessment of Motivation in Online Learning	p. 287
Mining Student Discussions for Profiling Participation and Scaffolding Learning	p. 299
Analysis of Log Data from a Web-Based Learning Environment: A Case Study	p. 311
Bayesian Networks and Linear Regression Models of Students' Goals, Moods, and Emotions	p. 323
Capturing and Analyzing Student Behavior in a Virtual Learning Environment: A Case Study on Usage of Library Resources	p. 339
Anticipating Students' Failure As Soon As Possible	p. 353
Using Decision Trees for Improving AEH Courses	p. 365
Validation Issues in Educational Data Mining: The Case of HTML-Tutor and iHelp	p. 377
Lessons from Project LISTEN's Session Browser	p. 389
Using Fine-Grained Skill Models to Fit Student Performance with Bayesian Networks	p. 417
Mining for Patterns of Incorrect Response in Diagnostic Assessment Data	p. 427
Machine-Learning Assessment of Students' Behavior within Interactive Learning Environments	p. 441
Learning Procedural Knowledge from User Solutions to III-Defined Tasks in a Simulated Robotic Manipulator	p. 451
Using Markov Decision Processes for Automatic Hint Generation	p. 467
Data Mining Learning Objects	p. 481
An Adaptive Bayesian Student Model for Discovering the Student's Learning Style and Preferences	p. 493
Index	p. 505
Table of Contents provided by Ingram. All Rights Reserved.

Handbook of Educational Data Mining

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

You Can Find This Book In

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