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Handbook of Educational Data Mining

Handbook of Educational Data Mining

Essential Research Book in Handbook of Educational Data Mining

Author(s) Name:  Cristobal Romero, Sebastian Ventura, Mykola Pechenizkiy, Ryan S.J.d. Baker

About the Book:

   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.
   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.

Table of Contents

  • Basic Techniques, Surveys, and Tutorials
  • Visualization in Educational Environments
  • A Data Repository for the EDM Community: The PSLC DataShop
  • Process Mining from Educational Data
  • Modeling Hierarchy and Dependence among Task Responses in EDM
  • Novel Derivation and Application of Skill Matrices: The q-Matrix Method
  • EDM to Support Group Work in Software Development Projects
  • Multi-Instance Learning versus Single-Instance Learning for Predicting the Student-s Performance
  • Modeling Affect by Mining Students Interactions within Learning Environments
  • Measuring Correlation of Strong Symmetric Association Rules in Educational Data
  • Mining Student Discussions for Profiling Participation and Scaffolding Learning
  • Mining for Patterns of Incorrect Response in Diagnostic Assessment Data
  • Machine-Learning Assessment of Students Behavior within Interactive Learning Environments
  • Learning Procedural Knowledge from User Solutions to Ill-Defined Tasks in a Simulated Robotic Manipulator
  • Data Mining Learning Objects
  • An Adaptive Bayesian Student Model for Discovering the Students Learning Style and Preferences

    • ISBN:   9781439804575

    Publisher:  CRC Press

    Year of Publication:  2010

    Book Link:  Home Page Url