Improving Intelligent Tutoring Systems: Using Expectation Maximization to Learn Student Skill Levels

  • Kimberly Ferguson
  • Ivon Arroyo
  • Sridhar Mahadevan
  • Beverly Woolf
  • Andy Barto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4053)


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.


Bayesian Network Bayesian Information Criterion Skill Model Intelligent Tutor System Pythagorean Theorem 
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

  • Kimberly Ferguson
    • 1
  • Ivon Arroyo
    • 1
  • Sridhar Mahadevan
    • 1
  • Beverly Woolf
    • 1
  • Andy Barto
    • 1
  1. 1.Computer Science DepartmentUniversity of Massachusetts AmherstAmherstUSA

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