Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/7632
Title: Multimodal Deep Learning Model for Detecting Types of Interactions for Regulation in Collaborative Learning
Authors: Nguyen, Andy
Järvelä, Sanna
Wang, Yansen
Rosé, Carolyn
Malmberg, Jonna
Järvenoja, Hanna
Keywords: Learning Sciences
Issue Date: Jun-2021
Publisher: International Society of the Learning Sciences
Citation: Nguyen, A., Järvelä, S., Wang, Y., Rosé, C., Malmberg, J., & Järvenoja, H. (2021). Multimodal Deep Learning Model for Detecting Types of Interactions for Regulation in Collaborative Learning. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. (pp. 941-942). Bochum, Germany: International Society of the Learning Sciences.
Abstract: This paper reports a design science research methodology (DSRM) study that develops, demonstrates, and evaluates a deep learning model utilizing multimodal data to automatically detect types of interactions for regulation in collaborative learning (RegCL) by using features extracted from electrodermal activity (EDA), video, and audio data involving secondary school students (N = 94). RegCL offers novel and essential opportunities to advance research on Socially Shared Regulation of Learning in groups (SSRL).
URI: https://doi.dx.org/10.22318/icls2021.941
https://repository.isls.org//handle/1/7632
Appears in Collections:ISLS Annual Meeting 2021

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