Advice-Giving between Young Learners in Robot-Assisted Language Learning




Honkalammi Hilla-Marja, Veivo Outi, Johansson Marjut

Sarah Warchhold, Daniel Duran,Iona Gessinger, Eran Raveh, Hyro AI

Human Perspectives on Spoken Human-Machine Interaction

Freiburg

2022

Proceedings of the Conference : Human Perspectives on Spoken Human-Machine Interaction

46

51

DOIhttps://doi.org/10.6094/UNIFR/223816

https://freidok.uni-freiburg.de/data/223816

https://research.utu.fi/converis/portal/detail/Publication/174828057



Social robots bring new possibilities to education. This paper presents an analysis of young learners’ interactions in robot-assisted language learning (RALL) and seeks to describe how they give each other advice in foreign language speaking situations. Especially when a problem arises due to insufficient languages skills, the learners engage in problem-solving negotiations. The data consist of eight video-recorded learning situations where eight pairs of 10 to 12 years old children interact in English as a foreign language (EFL) with a robot. This paper presents microanalyses on advice-giving situations where the learners help each other and succeed in their common task of answering the robot’s questions correctly. These microanalyses show that the learners give each other normative and epistemic advice. The results of the present study suggest that interaction problems in RALL situations lead to fruitful problem-solving interactions between the learners.


Last updated on 2024-26-11 at 14:25