Comparison of machine learning methods for intelligent tutoring systems

Authors:
Wilhelmiina Hämäläinen;Mikko Vinni
Affiliations:
Department of Computer Science, University of Joensuu, Joensuu, Finland;Department of Computer Science, University of Joensuu, Joensuu, Finland
Venue:
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Year:
2006

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Abstract

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.