Using self-determination theory in social robots to increase motivation in L2 word learning

Peggy Van Minkelen, Carmen Gruson, Pleun Van Hees, Mirle Willems, Jan De Wit, Jaap Denissen, Paul Vogt

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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.

Original languageEnglish
Title of host publicationHRI 2020 - Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction
PublisherIEEE Computer Society
Pages369-377
Number of pages9
ISBN (Electronic)9781450367462
DOIs
Publication statusPublished - 9 Mar 2020
Externally publishedYes
Event15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020 - Cambridge, United Kingdom
Duration: 23 Mar 202026 Mar 2020

Publication series

NameACM/IEEE International Conference on Human-Robot Interaction
ISSN (Electronic)2167-2148

Conference

Conference15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020
Country/TerritoryUnited Kingdom
CityCambridge
Period23/03/2026/03/20

Bibliographical note

Funding Information:
The authors would like to thank Mirjam de Haas for providing her scheme and training to code task engagement, all reviewers for their valuable feedback, and all participating children, their parents, and the schools for their contribution to this experiment. The first author was supported by a research trainee grant provided by Tilburg University.

Publisher Copyright:
© 2020 Association for Computing Machinery.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Human-robot interaction
  • Motivation
  • Robot tutor
  • Second language learning
  • Self-determination theory
  • Task engagement

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