Learning by teaching agents

Abstract

We present the design and implementation of an intelligent learning environment using an innovative multi-agent architecture scheme derived from the learning by teaching paradigm. Sixth grade students, who are domain-novices, take on the challenge of teaching a computer-based software agent how to solve distance-rate-time problems by constructing graphs, and learn about the domain in this process. The system was evaluated in a Metro Nashville 6 th grade classroom. Our experiments contrasted the learning, transfer, and motivation of students in two conditions: (1) those who learnt for themselves and (2) those who learnt by teaching agents within our environment. Our results showed that both groups improved in their word problem solving abilities, but there were no significant differences in the performance of the two groups. However, students in the learning by teaching condition demonstrated more motivation to learn, and showed better ability to transfer their knowledge of rate problems to a second domain. The motivation to learn was derived from self-reporting measures that included higher task value, self-regulation, self-efficacy, and critical thinking. The ability to transfer was measured in terms of students performance on rate problems associated with filling measurement cylinders with water. In addition, a survey conducted at the end of the study established that students who taught reported that they liked the system better and had more fun using it for problem solving tasks. However, we also found that our implementation of the learning by teaching approach resulted in the students having to spend a lot of additional time in interacting with the teachable agent using menu-based dialog structures. This is an issue that we will address in future designs of this system. Overall, the results of our study confirmed that interacting with teachable social agents had influenced middle school students positively, which also confirmed the viability of the learning by teaching agents approach and our design of the teachable agent.