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.
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Hämäläinen, W., Vinni, M. (2006). Comparison of Machine Learning Methods for Intelligent Tutoring Systems. In: Ikeda, M., Ashley, K.D., Chan, TW. (eds) Intelligent Tutoring Systems. ITS 2006. Lecture Notes in Computer Science, vol 4053. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11774303_52
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DOI: https://doi.org/10.1007/11774303_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35159-7
Online ISBN: 978-3-540-35160-3
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