A Metacognitive ACT-R Model of Students' Learning Strategies in Intelligent Tutoring Systems

Research has shown that students’ problem-solving actions vary in type and duration. Among other causes, this behavior is a result of strategies that are driven by different goals. We describe a first version of a computational cognitive model that explains the origin of these strategies and identifies the tendencies of students towards different learning goals. Our model takes into account (i) interpersonal differences, (ii) an estimation of the student’s knowledge level, and (iii) current feedback from the tutor, in order to predict the next action of the student – a solution, a guess or a help request. Our long-term goal is to use identification of the students’ strategies and their efficiency in order to better understand the learning process and to improve the metacognitive learning skills of the students.

• Type: Conference
• Year: 2004
• Where: ITS
• Authors: Ido Roll, Ryan Shaun Baker, Vincent Aleven, Kenneth R. Koedinger
• Keywords: Artificial Intelligence, Computational Cognitive Model, ITS 2004, Strategies, Student’s Knowledge Level