Febriantoro, Wicaksono; Gauthier, Andrea; Cukurova, Mutlu; (2023) The Promise of Physiological Data in Collaborative Learning: A Systematic Literature Review. In: Viberg, Olga and Jivet, Ioana and Muñoz-Merino, Pedro J and Perifanou, Maria and Papathoma, Tina, (eds.) Responsive and Sustainable Educational Futures. 18th European Conference on Technology Enhanced Learning, EC-TEL 2023, Aveiro, Portugal, September 4–8, 2023, Proceedings. (pp. pp. 75-88). Springer: Cham, Switzerland.

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Abstract

Collaborative learning is an important approach in education. Researchers are increasingly interested in using physiological data, such as Electrodermal Activity (EDA), as an objective tool to measure bodily reactions during collaborative activities. However, it remains unclear how physiological data can contribute to our understanding, monitoring and support of the collaborative learning process. To address this gap, a Systematic Literature Review (SLR) was conducted, focusing on the contribution of physiological data to collaborative learning, the features of physiological data that correlate with effective outcomes, and interventions designed to support collaboration based on physiological data. The review identified 13 relevant publications that revealed physiological data can indeed be useful for detecting certain aspects of collaboration including students’ cognitive, behavioral, and affective (emotion and motivation) states. Physiological arousal in the form of EDA peaks and physiological synchrony (interdependence or associated activity between individuals’ physiological signals) were the most commonly used features. Surprisingly, only one publication presented a prototype of a learning analytics dashboard that used physiological data to guide student reflections. Furthermore, the review highlights the potential for integrating physiological measures with other data sources, such as speech, eye gaze, and facial expression, to uncover psychophysiological reactions and accompanying social and contextual processes related to collaborative learning. Future research should consider embedding methods for the physiological detection and modeling of learning constructs within explicit, feedback-driven interventions for collaborative learning.

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