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Discovering Prediction Rules in AHA! Courses

  • Cristóbal Romero
  • Sebastián Ventura
  • Paul de Bra
  • Carlos de Castro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2702)

Abstract

In this paper we are going to show how to discover interesting prediction rules from student usage information to improve adaptive web courses. We have used AHA! to make courses that adapt both the presentation and the navigation depending on the level of knowledge that each particular student has. We have performed several modifications in AHA! to specialize it and power it in the educational area. Our objective is to discover relations between all the picked-up usage data (reading times, difficulty levels and test results) from student executions and show the most interesting to the teacher so that he can carry out the appropriate modifications in the course to improve it.

Keywords

Evolutionary Algorithm Domain Model User Model Usage Information Prediction Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Cristóbal Romero
    • 1
  • Sebastián Ventura
    • 1
  • Paul de Bra
    • 2
  • Carlos de Castro
    • 1
  1. 1.University of CórdobaCórdobaEspaña
  2. 2.Eindhoven University of Technology (TU/e)EindhovenThe Netherlands

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