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Title: Investigating collaborative learning success with physiological coupling indices based on electrodermal activity
Authors: Pijeira-díaz, Hector
Drachsler, Hendrik
Järvelä, Sanna
Kirschner, Paul A.
Keywords: multimodal
learning analytics
Issue Date: 16-Apr-2016
Publisher: ACM
Citation: Pijeira-díaz, H. J., Drachsler, H., Järvelä, S., & Kirschner, P. A. (2016). Investigating collaborative learning success with physiological coupling indices based on electrodermal activity. In Proceedings of the Sixth International Conference on Learning Analytics and Knowledge (LAK'2016) (pp. 64-73). (April 25-29, 2016, Edinburgh, UK). New York, NY, USA: ACM.
Abstract: Collaborative learning is considered a critical 21st century skill. Much is known about its contribution to learning, but still investigating a process of collaboration remains a challenge. This paper approaches the investigation on collaborative learning from a psychophysiological perspective. An experiment was set up to explore whether biosensors can play a role in analysing collaborative learning. On the one hand, we identified five physiological coupling indices (PCIs) found in the literature: 1) Signal Matching (SM), 2) Instantaneous Derivative Matching (IDM), 3) Directional Agreement (DA), 4) Pearson's correlation coefficient (PCC) and the 5) Fisher's z-transform (FZT) of the PCC. On the other hand, three collaborative learning measurements were used: 1) collaborative will (CW), 2) collaborative learning product (CLP) and 3) dual learning gain (DLG). Regression analyses showed that out of the five PCIs, IDM related the most to CW and was the best predictor of the CLP. Meanwhile, DA predicted DLG the best. These results play a role in determining informative collaboration measures for designing a learning analytics, biofeedback dashboard.
ISBN: 978-1-4503-4190-5
Appears in Collections:1. TELI Publications, books and conference papers

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