Modeling and Measuring Self-Regulated Learning in Teachable Agent Environments

Abstract

Our learning-by-teaching environment has students take on the role and responsibilities of a teacher to a virtual student named Betty. The environment is designed to help students learn and understand science topics for themselves as they teach and monitor their agent. This process is supported by adaptive scaffolding and feedback through interactions with the teachable agent and a mentor agent. This paper discusses the results of a comparative study conducted in an 8th-grade science classroom, where students received two kinds of metacognitive and learning strategy feedback. We analyze student performance and learning gains as a result of the intervention. To gain further insight into student learning behaviors exhibited during the intervention, we employ a data mining methodology incorporating hidden Markov modeling and sequence mining techniques. The results illustrate both the effectiveness of the experimental agent feedback in encouraging metacognitive learning strategies and the utility of the data mining methodology.