Comparison of Machine Learning Methods for Intelligent Tutoring Systems

  • Wilhelmiina Hämäläinen
  • Mikko Vinni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4053)


To implement real intelligence or adaptivity, the models for intelligent tutoring systems should be learnt from data. However, the educational data sets are so small that machine learning methods cannot be applied directly. In this paper, we tackle this problem, and give general outlines for creating accurate classifiers for educational data. We describe our experiment, where we were able to predict course success with more than 80% accuracy in the middle of course, given only hundred rows of data.


Support Vector Machine Bayesian Network Association Rule Machine Learn Method Intelligent Tutoring System 
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 2006

Authors and Affiliations

  • Wilhelmiina Hämäläinen
    • 1
  • Mikko Vinni
    • 1
  1. 1.Department of Computer ScienceUniversity of JoensuuJoensuuFinland

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