Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/1652
Title: Towards Automatic and Pervasive Physiological Sensing of Collaborative Learning
Authors: Sharma, Kshitij
Pappas, Ilias
Papavlasopoulou, Sofia
Giannakos, Michail
Issue Date: Jun-2019
Publisher: International Society of the Learning Sciences (ISLS)
Citation: Sharma, K., Pappas, I., Papavlasopoulou, S., & Giannakos, M. (2019). Towards Automatic and Pervasive Physiological Sensing of Collaborative Learning. In Lund, K., Niccolai, G. P., Lavoué, E., Gweon, C. H., & Baker, M. (Eds.), A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings, 13th International Conference on Computer Supported Collaborative Learning (CSCL) 2019, Volume 2 (pp. 684-687). Lyon, France: International Society of the Learning Sciences.
Abstract: We present a collaborative learning study contextualized within Project based Learning. The main aim of our contribution is to use the physiological data such as heart rate, skin temperature, electrodermal activity and blood volume pressure to quantify the learning experiences of the collaborating teams. We propose an automatic method to extract collaborative measures and study their relationship with the perceived performance, usefulness and satisfaction from the collaborative sessions from various student groups in a university degree course. We aim to contribute towards automatized, pervasive and more generalizable sensing of collaborative learning. Our results show that the synchrony in automatically extracted physiological states correlates positively with perceived performance and satisfaction of teams.
URI: https://doi.dx.org/10.22318/cscl2019.684
https://repository.isls.org//handle/1/1652
Appears in Collections:CSCL 2019

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