Krittaya Leelawong

LEARNING BY TEACHING: A NEW AGENT PARADIGM FOR EDUCATIONAL SOFTWARE

ABSTRACT This paper discusses Bettys Brain, a teachable agent in the domain of river ecosystems that combines learning by teaching with self-regulation mentoring to promote deep learning and understanding. Two studies demonstrate the effectiveness of this system. The first study focused on components that define student-teacher interactions in the learning by teaching task. The second study examined the value of adding meta-cognitive strategies that governed Bettys behavior and self-regulation hints provided by a mentor agent. The study compared three versions: a system where the student was tutored by a pedagogical agent, a learning by teaching system, where students taught a baseline version of Betty, and received tutoring help from the mentor, and a learning by teaching system, where Betty was enhanced to include self-regulation strategies, and the mentor provided help on domain material on how to become better learners and better teachers. Results indicate that the addition of the self-regulated Betty and the self-regulation mentor better prepared students to learn new concepts later, even when they no longer had access to the SRL environment.

Intelligent user interface design for teachable agent systems

This paper describes the interface components for a system called Bettys Brain, an intelligent agent we have developed for studying the learning by teaching paradigm. Our previous studies have shown that students gain better understanding of domain knowledge when they prepare to teach others versus when they prepare to take an exam. This finding has motivated us to develop computer agents that students teach using concept map representations with a visual interface. Betty is intelligent not because she learns on her own, but because she can apply qualitative-reasoning techniques to answer questions that are directly related to what she has been taught through the concept map. We evaluate the agents interfaces in terms of how well they support learning activities, using examples of their use by fifth grade students in an extensive study that we performed in a Nashville public school. A critical analysis of the outcome of our studies has led us to propose the next generation interfaces in a multi-agent paradigm that should be more effective in promoting constructivist learning and self-regulation in the learning by teaching framework

Developing Learning by Teaching Environments That Support Self-Regulated Learning

Betty’s Brain is a teachable agent system in the domain of river ecosystems that combines learning by teaching and self-regulation strategies to promote deep learning and understanding. Scaffolds in the form of hypertext resources, a Mentor agent, and a set of quiz questions help novice students learn and self-assess their own knowledge. The computational architecture is implemented as a multi-agent system to allow flexible and incremental design, and to provide a more realistic social context for interactions between students and the teachable agent. An extensive study that compared three versions of this system: a tutor only version, learning by teaching, and learning by teaching with self-regulation strategies demonstrates the effectiveness of learning by teaching environments, and the impact of self-regulation strategies in improving preparation for learning among novice learners.

A multi-agent architecture implementation of learning by teaching systems

Our group has been designing and implementing learning environments that promote deep understanding and transfer in complex domains. We have adopted the learning by teaching paradigm, and developed computer-based agents that students teach, and learn from this experience. The success of teachable agents has led us to develop a multiagent architecture that will be used to develop extended instructional systems based on gaming environments.