Year: 2006
Type: article
Abstract: The research reported in this paper focuses on the hypothesis that an intelligent tutoring system that provides guidance with respect to students’ meta-cognitive abilities can help them to become better learners. Our strategy is to extend a Cognitive Tutor (Anderson, Corbett, Koedinger, & Pelletier, 1995) so that it not only helps students acquire domain-specific skills, but also develop better general help-seeking strategies. In developing the Help Tutor, we used the same Cognitive Tutor technology at the metacognitive level that has been proven to be very effective at the cognitive level. A key challenge is to develop a model of how students should use a Cognitive Tutor’s help facilities. We created a preliminary model, implemented by 57 production rules that capture both effective and ineffective help-seeking behavior. As a first test of the model’s efficacy, we used it off-line to evaluate students’ help-seeking behavior in an existing data set of student-tutor interactions. We then refined the model based on the results of this analysis. Finally, we conducted a pilot study with the Help Tutor involving four students. During one session, we saw a statistically significant reduction in students’ meta-cognitive error rate, as determined by the Help Tutor’s model. These preliminary results inspire confidence as we gear up for a larger-scale controlled experiment to evaluate whether tutoring on help seeking has a positive effect on students’ learning outcomes.
Institution: Carnegie Mellon University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W2158703238