Abstract
Over the last decade, there has been a renewed interest in capturing 21st century skills using new data collection tools. In this paper, we leverage an existing dataset where multimodal sensors (mobile eye- trackers, motion sensors, galvanic skin response wristbands) were used to identify markers of productive collaborations. The data came from 42 pairs (N = 84) of participants who had no coding experience. They were asked to program a robot to solve a variety of mazes. We explored four different measures of physiological synchrony: Signal Matching (SM), Instantaneous Derivative Matching (IDM), Directional Agreement (DA) and Pearson’s Correlation (PC). Overall, we found PC to be positively associated with learning gains and DA with collaboration quality. We compare those results with prior studies and discuss implications for measuring collaborative process through physiological sensors.
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Dich, Y., Reilly, J., Schneider, B. (2018). Using Physiological Synchrony as an Indicator of Collaboration Quality, Task Performance and Learning. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10947. Springer, Cham. https://doi.org/10.1007/978-3-319-93843-1_8
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DOI: https://doi.org/10.1007/978-3-319-93843-1_8
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