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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Domingos, P., Pazzani, M.: On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29, 103–130 (1997)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29(2-3), 131–163 (1997)
Geiger, D., Heckerman, D.: Knowledge representation and inference in similarity networks and Bayesian multinets. Artificial Intelligence 82, 45–74 (1996)
Huber, P.J.: Robust estimation of a location parameter. Annals of mathematical statistics 35, 73–101 (1964)
Kotsiantis, S.B., Pierrakeas, C.J., Pintelas, P.E.: Preventing student dropout in distance learning using machine learning techniques. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS, vol. 2773, pp. 267–274. Springer, Heidelberg (2003)
Minaei-Bidgoli, B., Kashy, D.A., Kortemeyer, G., Punch, W.: Predicting student performance: an application of data mining methods with an educational web-based system. In: Proceedings of 33rd Frontiers in Education Conference, pp.T2A13–T2A18 (2003)
Vapnik, V.N.: Statistical learning theory. John Wiley & Sons, Chichester (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)