Student Modeling with Atomic Bayesian Networks

  • Fang Wei
  • Glenn D. Blank
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4053)


Atomic Bayesian Networks (ABNs) combine several valuable features in student models: prerequisite relationships, concept to solution step relationships, and real time responsiveness. Recent work addresses some of these features but have not combined them, which we believe is necessary in an ITS that helps students learn in a complex domain, in our case, object-oriented design. A refined representation of prerequisite relationships considers relationships between concepts as explicit knowledge units. Theorems show how to reduce the number of parameters required to a small constant, so that each ABN can guarantee a real time response. We evaluated ABN-based student models with 240 simulated students, investigating their behavior for different types of students and different slip and guess values. Holding slip and guess to equal, small values, ABNs are able to produce accurate diagnostic rates for student knowledge states.


Bayesian Network Knowledge Level Knowledge State Intelligent Tutoring System Student Model 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fang Wei
    • 1
  • Glenn D. Blank
    • 1
  1. 1.Computer Science & EngineeringLehigh UniversityBethlehemUSA

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