Using Self-Determination Theory in Social Robots to Increase Motivation in L2 Word Learning

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. CCS CONCEPTS • Human-centered computing → Empirical studies in HCI; • Applied computing → Interactive learning environments. ACM Reference Format: Peggy van Minkelen, Carmen Gruson, Pleun van Hees, Mirle Willems, Jan de Wit, Rian Aarts, Jaap Denissen, and Paul Vogt. 2020. Using Self-Determination Theory in Social Robots to Increase Motivation in L2 Word Learning. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’20), March 23–26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3319502.3374828