Using self-determination theory in social robots to increase motivation in L2 word learning
Publication date
2020
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Abstract
This study presents a second language word learning experiment using a social robot with motivational strategies. These strategies were implemented in a social robot tutor to stimulate preschool children's intrinsic motivation. Subsequently, we investigated their effect on children's task engagement and word learning performance. The strategies were derived from the Self-Determination Theory, a well-known psychological theory that assumes that intrinsic motivation is strongly related to the fulfilment of three basic human needs, namely the need for autonomy, competence, and relatedness. We found an increase in the strength and duration of task engagement when all three psychological needs were supported by the robot. However, no significant results for learning gains were observed. Our intervention appears a promising method for improving child-robot interactions in educational settings, especially to sustain in long-term interactions.
Keywords
Human-robot interaction, Motivation, Robot tutor, Second language learning, Self-determination theory, Task engagement, Artificial Intelligence, Human-Computer Interaction, Electrical and Electronic Engineering
Citation
van Minkelen, P, Gruson, C, van Hees, P, Willems, M, de Wit, J, Aarts, R, Denissen, J J A & Vogt, P 2020, Using self-determination theory in social robots to increase motivation in L2 word learning. in HRI '20: Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction. Association for Computing Machinery, New York, pp. 369-377, 15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020, Cambridge, United Kingdom, 23/03/20. https://doi.org/10.1145/3319502.3374828, conference