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Paper Number
2415
Paper Type
Complete
Description
Emotional regulation in learning has been recognised as a critical factor for collaborative learning success. However, the “unobservable” processes of emotion and motivation at the core of learning regulation have challenged the methodological progress to examine and support learners’ regulation. Artificial intelligence (AI) and learning analytics have recently brought novel opportunities for investigating the learning processes. This multidisciplinary study proposes a novel fine-grained approach to provide empirical evidence on the application of these advanced technologies in assessing emotional regulation in synchronous computer-support collaborative learning (CSCL). The study involved eighteen university students (N=18) working collaboratively in groups of three. The process mining analysis was adopted to explore the patterns of emotional regulation in synchronous CSCL, while AI facial expression recognition was used for examining learners’ associated emotions and emotional synchrony in regulatory activities. Our findings establish a foundation for further design of human-centred AI-enhanced support for collaborative learning regulation.
Recommended Citation
Nguyen, Andy; Hong, Yvonne; Dang, Belle; and Nguyen, Phuong Thi Bich, "Emotional Regulation in Synchronous Online Collaborative Learning: A Facial Expression Recognition Study" (2022). ICIS 2022 Proceedings. 12.
https://aisel.aisnet.org/icis2022/learning_iscurricula/learning_iscurricula/12
Emotional Regulation in Synchronous Online Collaborative Learning: A Facial Expression Recognition Study
Emotional regulation in learning has been recognised as a critical factor for collaborative learning success. However, the “unobservable” processes of emotion and motivation at the core of learning regulation have challenged the methodological progress to examine and support learners’ regulation. Artificial intelligence (AI) and learning analytics have recently brought novel opportunities for investigating the learning processes. This multidisciplinary study proposes a novel fine-grained approach to provide empirical evidence on the application of these advanced technologies in assessing emotional regulation in synchronous computer-support collaborative learning (CSCL). The study involved eighteen university students (N=18) working collaboratively in groups of three. The process mining analysis was adopted to explore the patterns of emotional regulation in synchronous CSCL, while AI facial expression recognition was used for examining learners’ associated emotions and emotional synchrony in regulatory activities. Our findings establish a foundation for further design of human-centred AI-enhanced support for collaborative learning regulation.
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