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Diagnosing Self-efficacy in Intelligent Tutoring Systems: An Empirical Study

  • 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

Self-efficacy is an individual’s belief about her ability to perform well in a given situation. Because self-efficacious students are effective learners, endowing intelligent tutoring systems with the ability to diagnose self-efficacy could lead to improved pedagogy. Self-efficacy is influenced by (and influences) affective state. Thus, physiological data might be used to predict a students’ level of self-efficacy. This paper investigates an inductive approach to automatically constructing models of self-efficacy that can be used at runtime to inform pedagogical decisions. In an empirical study, two families of self-efficacy models were induced: a static model, learned solely from pre-test (non-intrusively collected) data, and a dynamic model, learned from both pre-test data as well as runtime physiological data collected with a biofeedback apparatus. The resulting static model is able to predict students’ real-time levels of self-efficacy with reasonable accuracy, while the physiologically informed dynamic model is even more accurate.

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Authors and Affiliations

  1. Department of Computer Science, North Carolina State University, Raleigh, NC, 27695, USA

    Scott W. McQuiggan & James C. Lester

Authors
  1. Scott W. McQuiggan
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  2. James C. Lester
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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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McQuiggan, S.W., Lester, J.C. (2006). Diagnosing Self-efficacy in Intelligent Tutoring Systems: An Empirical Study. 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_56

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  • DOI: https://doi.org/10.1007/11774303_56

  • 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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