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Title: Exploring Socially Shared Regulation With an AI Deep Learning Approach Using Multimodal Data
Authors: Nguyen, Andy
Järvelä, Sanna
Wang, Yansen
Rosé, Carolyn P.
Keywords: Learning Sciences
Issue Date: 2022
Publisher: International Society of the Learning Sciences
Citation: Nguyen, A., Järvelä, S., Wang, Y., & Rosé, C. P. (2022). Exploring socially shared regulation with an AI deep learning approach using multimodal data. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.), Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022 (pp. 527-534). International Society of the Learning Sciences.
Abstract: Socially shared regulation of learning (SSRL) is essential for the success of collaborative learning, yet learners often neglect needed regulation while facing challenges. In order to provide targeted support when needed, it is critical to identify the precise events that trigger regulation. Multimodal collaborative learning data may offer opportunities for this. This study aims to lay such a foundation by exploring the potential for using machine-learned models trained on multimodal data, including electrodermal activities (EDA), speech, and video, to detect the presence of SSRL-relevant process-level indicators in successful and less successful groups. The study involves thirty groups of secondary students (N=94) working collaboratively in five physics lessons. Considering the demonstrated positive results of machine-learned models, the advantages and limitations of the technical approach are discussed, and further development directions are suggested.
Description: Long Paper
Appears in Collections:ISLS Annual Meeting 2022

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