Modeling self-efficacy in intelligent tutoring systems: An inductive approach

Abstract. Self-efficacy is an individual's belief about her ability to perform well in a given situation. Because selfefficacious students are effective learners, endowing intelligent tutoring systems with the ability to diagnose selfefficacy could lead to improved pedagogy. Self-efficacy is influenced by (and influences) affective state. Thus, physiological data might be used to predict a student's level of self-efficacy. This article investigates an inductive approach to automatically constructing models of self-efficacy that can be used at runtime to inform pedagogical decisions. It reports on two complementary empirical studies. In the first study, two families of self-efficacy models were induced: a static self-efficacy model, learned solely from pre-test (non-intrusively collected) data, and a dynamic self-efficacy model, learned from both pre-test data as well as runtime physiological data collected with a biofeedback apparatus. In the second empirical study, a similar...

• Type: Journal
• Year: 2008
• Where: UMUAI
• Authors: Scott W. McQuiggan, Bradford W. Mott, James C. Lester
• Keywords: Physiological Data, Self-efficacy, Self-efficacy Models, UMUAI 2008