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Toward Effective Robot--Child Tutoring

Toward Effective Robot--Child Tutoring Personalized learning environments have the potential to improve learning outcomes for children in a variety of educational domains, as they can tailor instruction based on the unique learning needs of individuals. Robot tutoring systems can further engage users by leveraging their potential for embodied social interaction and take into account crucial aspects of a learner, such as a student’s motivation in learning. In this article, we demonstrate that motivation in young learners corresponds to observable behaviors when interacting with a robot tutoring system, which, in turn, impact learning outcomes. We first detail a user study involving children interacting one on one with a robot tutoring system over multiple sessions. Based on empirical data, we show that academic motivation stemming from one’s own values or goals as assessed by the Academic Self-Regulation Questionnaire (SRQ-A) correlates to observed suboptimal help-seeking behavior during the initial tutoring session. We then show how an interactive robot that responds intelligently to these observed behaviors in subsequent tutoring sessions can positively impact both student behavior and learning outcomes over time. These results provide empirical evidence for the link between internal motivation, observable behavior, and learning outcomes in the context of robot--child tutoring. We also identified an additional suboptimal behavioral feature within our tutoring environment and demonstrated its relationship to internal factors of motivation, suggesting further opportunities to design robot intervention to enhance learning. We provide insights on the design of robot tutoring systems aimed to deliver effective behavioral intervention during learning interactions for children and present a discussion on the broader challenges currently faced by robot--child tutoring systems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2019 ACM
ISSN
2160-6455
eISSN
2160-6463
DOI
10.1145/3213768
Publisher site
See Article on Publisher Site

Abstract

Personalized learning environments have the potential to improve learning outcomes for children in a variety of educational domains, as they can tailor instruction based on the unique learning needs of individuals. Robot tutoring systems can further engage users by leveraging their potential for embodied social interaction and take into account crucial aspects of a learner, such as a student’s motivation in learning. In this article, we demonstrate that motivation in young learners corresponds to observable behaviors when interacting with a robot tutoring system, which, in turn, impact learning outcomes. We first detail a user study involving children interacting one on one with a robot tutoring system over multiple sessions. Based on empirical data, we show that academic motivation stemming from one’s own values or goals as assessed by the Academic Self-Regulation Questionnaire (SRQ-A) correlates to observed suboptimal help-seeking behavior during the initial tutoring session. We then show how an interactive robot that responds intelligently to these observed behaviors in subsequent tutoring sessions can positively impact both student behavior and learning outcomes over time. These results provide empirical evidence for the link between internal motivation, observable behavior, and learning outcomes in the context of robot--child tutoring. We also identified an additional suboptimal behavioral feature within our tutoring environment and demonstrated its relationship to internal factors of motivation, suggesting further opportunities to design robot intervention to enhance learning. We provide insights on the design of robot tutoring systems aimed to deliver effective behavioral intervention during learning interactions for children and present a discussion on the broader challenges currently faced by robot--child tutoring systems.

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS)Association for Computing Machinery

Published: Feb 11, 2019

Keywords: Child--robot interaction

References