Facial Expression Recognition for Examining Emotional Regulation in Synchronous Online Collaborative Learning
Ngo, Duong; Nguyen, Andy; Dang, Belle; Ngo, Ha (2024-01-02)
Springer
02.01.2024
Ngo, D., Nguyen, A., Dang, B. et al. Facial Expression Recognition for Examining Emotional Regulation in Synchronous Online Collaborative Learning. Int J Artif Intell Educ 34, 650–669 (2024). https://doi.org/10.1007/s40593-023-00378-7
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© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
https://creativecommons.org/licenses/by/4.0/
© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202405023069
https://urn.fi/URN:NBN:fi:oulu-202405023069
Tiivistelmä
Abstract
Artificial intelligence (AI) has been recognised as a promising technology for methodological progress and theoretical advancement in learning sciences. However, there remains few empirical investigations into how AI could be applied in learning sciences research. This study aims to utilize AI facial recognition to inform the learning regulation behaviors in synchronous online collaborative learning environments. By studying groups of university students (N = 36) who participated in their online classes under the COVID-19 social distancing mandates, we strive to understand the interrelation between individual affective states and their collaborative group members. Theoretically underpinned by the socially shared regulation of learning framework, our research features a cutting-edge insight into how learners socially shared regulation in group-based tasks. Findings accentuate fundamental added values of AI application in education, whilst indicating further interesting patterns about student self-regulation in the collaborative learning environment. Implications drawn from the study hold strong potential to provide theoretical and practical contributions to the exploration of AI supportive roles in designing and personalizing learning needs, as well as fathom the motion and multiplicity of collaborative learning modes in higher education.
Artificial intelligence (AI) has been recognised as a promising technology for methodological progress and theoretical advancement in learning sciences. However, there remains few empirical investigations into how AI could be applied in learning sciences research. This study aims to utilize AI facial recognition to inform the learning regulation behaviors in synchronous online collaborative learning environments. By studying groups of university students (N = 36) who participated in their online classes under the COVID-19 social distancing mandates, we strive to understand the interrelation between individual affective states and their collaborative group members. Theoretically underpinned by the socially shared regulation of learning framework, our research features a cutting-edge insight into how learners socially shared regulation in group-based tasks. Findings accentuate fundamental added values of AI application in education, whilst indicating further interesting patterns about student self-regulation in the collaborative learning environment. Implications drawn from the study hold strong potential to provide theoretical and practical contributions to the exploration of AI supportive roles in designing and personalizing learning needs, as well as fathom the motion and multiplicity of collaborative learning modes in higher education.
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