Curiosity Notebook

A Platform for Learning by Teaching Conversational Agents

Abstract

Learning by teaching is an established pedagogical technique; however, the exact process through which learning happens remains difficult to assess, in part due to the variability in the tutor-tutee pairing and interaction. Prior research proposed the use of teachable agents acting as students, in order to facilitate more controlled studies of the learning by teaching phenomenon. In this work, we introduce a learning by teaching platform, Curiosity Notebook, which allows students to work individually or in groups to teach a conversational agent a classification task in a variety of subject topics. We conducted a 4-week exploratory study with 12 fourth and fifth grade elementary school children, who taught a conversational robot how to classify animals, rocks/minerals and paintings. This paper outlines the architecture of our system, describes the lessons learned from the study, and contributes design considerations on how to design conversational agents and applications for learning by teaching scenarios.

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Cite this work

@inproceedings{law2020curiosity, title={Curiosity Notebook: A Platform for Learning by Teaching Conversational Agents}, author={Law, Edith and Baghaei Ravari, Parastoo and Chhibber, Nalin and Kulic, Dana and Lin, Stephanie and Pantasdo, Kevin D and Ceha, Jessy and Suh, Sangho and Dillen, Nicole}, booktitle={Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems}, pages={1--9}, year={2020} }