Case Studies in Learning by Teaching Behavioral Differences in Directed versus Guided Learning

Our studies with Betty's Brain, a learning by teaching environment, have shown that the system is effective in helping fifth grade students gain a good understanding of river ecosystem concepts. The use of self-regulation strategies demonstrated that the learning gains transferred to new domains where students worked without the self-regulation system. This paper analyzes the log files of the student activities to determine which activities in the learning environment contribute to the students developing metacognitive strategies that contribute to their preparation for future learning

Incorporating self regulated learning techniques into learning by teaching environments

This paper discusses Betty’s Brain, a teachable agent in the domain of 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 metacognitive strategies that governed Betty’s behavior and selfregulation hints provided by a mentor agent. The study compared three versions: an intelligent tutoring version, a learning by teaching version, and a learning by teaching plus selfregulation strategies. Results indicate that the addition of the self-regulation mentor better prepared students to learn new concepts later, even when they no longer had access to the self-regulation environment

Teachable Agents: Learning by Teaching Environments for Science Domains

The crisis in science education and the need for innovative computer-based learning environments has prompted us to develop a multi-agent system, Betty’s Brain that implements the learning by teaching paradigm. The design and implementation of the system based on cognitive science and education research in constructivist, inquiry-based learning, involves an intelligent software agent, Betty, 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. The results of an extensive study in a fifth grade classroom of a Nashville public school has demonstrated impressive results in terms of improved motivation and learning gains. Reflection on the results has prompted us to develop a new version of this system that focuses on formative assessment and the teaching of selfregulated strategies to improve students ’ learning, and promote better understanding and transfer.