article

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

Authors:

Krittaya Leelawong,

Gautam BiswasAuthors Info & Claims

International Journal of Artificial Intelligence in Education, Volume 18, Issue 3

Pages 181 - 208

Published: 01 August 2008 Publication History

Abstract

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.

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T S ABiswas G(2024)Relating Students Cognitive Processes and Learner-Centered Emotions: An Advanced Deep Learning ApproachProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3685751(575-584)Online publication date: 4-Nov-2024
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Schmucker RXia MAzaria AMitchell TJoyner DKim MWang XXia M(2024)Ruffle&Riley: From Lesson Text to Conversational TutoringProceedings of the Eleventh ACM Conference on Learning @ Scale10.1145/3657604.3664719(547-549)Online publication date: 9-Jul-2024
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Index Terms

Designing Learning by Teaching Agents: The Betty's Brain System
1. Applied computing
  1. Education
  2. Enterprise computing
2. Computing methodologies
  1. Artificial intelligence
  2. Modeling and simulation
    1. Model development and analysis

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cover image International Journal of Artificial Intelligence in Education

International Journal of Artificial Intelligence in Education Volume 18, Issue 3

August 2008

107 pages

ISSN:1560-4292

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IOS Press

Netherlands

Publication History

Published: 01 August 2008

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Cited By

T S ABiswas G(2024)Relating Students Cognitive Processes and Learner-Centered Emotions: An Advanced Deep Learning ApproachProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3685751(575-584)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3678957.3685751
Oyedokun OMubarrat SJoshi AMousas CKao D(2024)Exploring the Influence of Avatar Skin Tone in VR Educational GamesCompanion Proceedings of the 2024 Annual Symposium on Computer-Human Interaction in Play10.1145/3665463.3678799(227-234)Online publication date: 14-Oct-2024
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Cai ZPark SNixon NDoroudi S(2024)Advancing Knowledge Together: Integrating Large Language Model-based Conversational AI in Small Group Collaborative LearningExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650868(1-9)Online publication date: 11-May-2024
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Debbané ALee KTse JLaw E(2023)Learning by Teaching: Key Challenges and Design ImplicationsProceedings of the ACM on Human-Computer Interaction10.1145/35795017:CSCW1(1-34)Online publication date: 16-Apr-2023
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