Engagement in longitudinal child-robot language learning interactions: Disentangling robot and task engagement
Haas, Mirjam de; Vogt, Paul; Berghe, Rianne van den; Leseman, Paul; Oudgenoeg-Paz, Ora; Willemsen, Bram; Wit, Jan de; Krahmer, Emiel
(2022) International Journal of Child-Computer Interaction, volume 33, pp. 1 - 16
(Article)
Abstract
This study investigated a seven sessions interaction between a peer-tutor robot and Dutch preschoolers (5 years old) during which the children learned English. We examined whether children’s engagement differed when interacting with a tablet and a robot using iconic gestures, with a tablet and a robot using no iconic gestures
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and with only a tablet. Two engagement types were annotated (task engagement and robot engagement) using a novel coding scheme based on an existing coding scheme used in kindergartens. The findings revealed that children’s task engagement dropped over time in all three conditions, consistent with the novelty effect. However, there were no differences between the different conditions for task engagement. Interestingly, robot engagement showed a difference between conditions. Children were more robot engaged when interacting with a robot using iconic gestures than without iconic gestures. Finally, when comparing children’s word knowledge with their engagement, we found that both task engagement and robot engagement were positively correlated with children’s word retention.
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Keywords: Child-robot interaction, Engagement, Second language learning, Robot tutor, Preschool children
ISSN: 2212-8689
Publisher: Elsevier BV
Note: Funding Information: This work has been supported by the EU H2020 L2TOR project (grant 688014 ). We would like to thank the children, their parents, and the schools for their participation. Furthermore, we would like to thank Laurette Gerts, Annabella Hermans, Esmee Kramer, Madee Kruijt, Marije Merckens, David Mogendorff, Sam Muntjewerf, Reinjet Oostdijk, Laura Pijpers, Chani Savelberg, Robin Sonders, Sirkka van Straalen, Sabine Verdult, Esmee Verheem, Pieter Wolfert, Hugo Zijlstra and Michelle Zomers for their help in collecting data and annotating videos. Publisher Copyright: © 2022 The Author(s)
(Peer reviewed)
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