Investigating collaborative learning success with physiological coupling indices based on electrodermal activity

Hector Pijeira-díaz, Hendrik Drachsler, Sanna Järvelä, Paul A. Kirschner

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


    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.
    Original languageEnglish
    Title of host publicationProceedings of the Sixth International Conference on Learning Analytics and Knowledge
    Place of PublicationNew York, USA
    PublisherAssociation for Computing Machinery (ACM)
    Number of pages10
    ISBN (Electronic)978-1-4503-4190-5
    Publication statusPublished - 25 Apr 2016
    EventThe 6th International Learning Analytics & Knowledge Conference - University of Edinburg, Edinburg, United Kingdom
    Duration: 25 Apr 201629 Apr 2016

    Publication series

    SeriesACM International Conference Proceeding Series


    ConferenceThe 6th International Learning Analytics & Knowledge Conference
    Abbreviated titleLAK16
    Country/TerritoryUnited Kingdom
    Internet address


    • multimodal
    • learning analytics
    • cscl
    • K12


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