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Probabilistic student models: Bayesian Belief Networks and Knowledge Space Theory

  • Michael Villano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 608)

Abstract

The applicability of Knowledge Space Theory (Falmagne and Doignon) and Bayesian Belief Networks (Pearl) as probabilistic student models imbedded in an Intelligent Tutoring System is examined. Student modeling issues such as knowledge representation, adaptive assessment, curriculum advancement, and student feedback are addressed. Several factors contribute to uncertainty in student modeling such as careless errors and lucky guesses, learning and forgetting, and unanticipated student response patterns. However, a probabilistic student model can represent uncertainty regarding the estimate of the student's knowledge and can be tested using empirical student data and established statistical techniques.

Keywords

Knowledge Structure Intelligent Tutoring System Student Model Bayesian Belief Network Knowledge Assessment 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Michael Villano
    • 1
  1. 1.Sensor and Systems Development CenterHoneywell Inc. 5MN65-2300MinneapolisUSA

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