Designing Learning by Teaching Agents: The Betty's Brain System

By Krittaya Leelawong, Gautam Biswas. International Journal of Artificial Intelligence in Education, 2008

371 citations recorded by the source corpus.

The idea that teaching others is a powerful way to learn is intuitively compelling and supported in the research literature. We have developed computer-based, domain-independent Teachable Agents that students can teach using a visual representation. The students query their agent to monitor their learning and problem solving behavior. This motivates the students to learn more so they can teach their agent to perform better. This paper presents a teachable agent called Betty's Brain that combines learning by teaching with self-regulated learning feedback to promote deep learning and understanding in science domains. A study conducted in a 5th grade science classroom compared three versions of the system: a version where the students were taught by an agent, a baseline learning by teaching version, and a learning by teaching version where students received feedback on self-regulated learning strategies and some domain content. In the other two systems, students received feedback primarily on domain content. Our results indicate that all three groups showed learning gains during a main study where students learnt about river ecosystems, but the two learning by teaching groups performed better than the group that was taught. These differences persisted in the transfer study, but the gap between the baseline learning by teaching and self-regulated learning group decreased. However, there are indications that self-regulated learning feedback better prepared students to learn in new domains, even when they no longer had access to the self-regulation environment.

Subfields: Artificial Intelligence, Computer Science Applications, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/2e83b05c5dbd26f54bd7dc59f7851d5771df34d4

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Citations in the corpus

Theoretical, conceptual, methodological, and instructional issues in research on metacognition and self-regulated learning: A discussion (2009, methodology): “These data can be correlated with learning outcomes and used for predictive purposes when used in conjunction with computer-based learning environments (e.g., Azevedo 2009; Leelawong and Biswas 2008; Graesser et al. 2005).”
AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring (2014, methodology): “As a comparison, Betty's Brain showed consistent learning gains using concept map tasks (Leelawong and Biswas 2008).”
Stupid Tutoring Systems, Intelligent Humans (2016, background): “2014); and simulated students that enable human students to learn by teaching (Leelawong and Biswas 2008; Matsuda et al. 2010).”
Seven Affordances of Computer-Supported Collaborative Learning: How to Support Collaborative Learning? How Can Technologies Help? (2016, positive): “Even within the small-group setting, students may collaborate with robots and agents in addition to or instead of humans (Leelawong & Biswas, 2008; Miyake & Okita, 2012).”