Improving intelligent tutoring systems: using expectation maximization to learn student skill levels

Authors:
Kimberly Ferguson;Ivon Arroyo;Sridhar Mahadevan;Beverly Woolf;Andy Barto
Affiliations:
Computer Science Department, University of Massachusetts Amherst, Amherst, MA;Computer Science Department, University of Massachusetts Amherst, Amherst, MA;Computer Science Department, University of Massachusetts Amherst, Amherst, MA;Computer Science Department, University of Massachusetts Amherst, Amherst, MA;Computer Science Department, University of Massachusetts Amherst, Amherst, MA
Venue:
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Year:
2006

Citing 3
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Abstract

This paper describes research to analyze students' initial skill level and to predict their hidden characteristics while working with an intelligent tutor. Based only on pre-test problems, a learned network was able to evaluate a students mastery of twelve geometry skills. This model will be used online by an Intelligent Tutoring System to dynamically determine a policy for individualizing selection of problems/hints, based on a students learning needs. Using Expectation Maximization, we learned the hidden parameters of several Bayesian networks that linked observed student actions with inferences about unobserved features. Bayesian Information Criterion was used to evaluate different skill models. The contribution of this work includes learning the parameters of the best network, whereas in previous work, the structure of a student model was fixed.