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

  • Scott W. McQuiggan
  • James C. Lester
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4053)


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.


Receiver Operating Characteristic Curve Galvanic Skin Response Intelligent Tutor System Online Tutorial Blood Volume Pulse 
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

  • Scott W. McQuiggan
    • 1
  • James C. Lester
    • 1
  1. 1.Department of Computer ScienceNorth Carolina State UniversityRaleighUSA

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