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Student Modeling with Atomic Bayesian Networks

  • Conference paper
Intelligent Tutoring Systems (ITS 2006)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 4053))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Computer Science & Engineering, Lehigh University, 19 Memorial Drive West, Bethlehem, PA, 18015, (001-610)-758-4867, (001-610)-758-4605, USA

    Fang Wei & Glenn D. Blank

Authors
  1. Fang Wei
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  2. Glenn D. Blank
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Editor information

Editors and Affiliations

  1. JAIST, 1-1, Asahi-dai, Nomi, 923-1292, Ishikawa, Japan

    Mitsuru Ikeda

  2. Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA, USA

    Kevin D. Ashley

  3. Graduate Institute of Network Learning Technology, National Central University, 300, Jhongda Rd., 32001, Jhongli City,Taoyuan County, Taiwan

    Tak-Wai Chan

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© 2006 Springer-Verlag Berlin Heidelberg

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Wei, F., Blank, G.D. (2006). Student Modeling with Atomic Bayesian Networks. 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_49

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  • DOI: https://doi.org/10.1007/11774303_49

  • 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)

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