Unpacking the relationship between existing and new measures of physiological synchrony and collaborative learning: a mixed methods study

Over the last decade, there has been a renewed interest in capturing twenty-first century skills using new data collection tools. In this article, we leverage an existing dataset where electrodermal activity (EDA) was used to identify markers of productive collaboration. The data came from 42 pairs of participants ( N  = 84) who had no coding experience and were asked to program a robot to solve a variety of mazes. Because little is known on how physiological synchrony relates to collaborative learning, we explored four different measures of 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 (r = 0.35) and DA with collaboration quality (r = 0.3). To gain further insights into these results, we also qualitatively analyzed two groups and identified situations with high or low physiological synchrony. We observed higher synchrony values when members of a productive group reacted to an external event (e.g., following instructions, receiving a hint), oscillations when they were watching a video or interacting with each other, and lower values when they were programming and / or seem to be confused. Based on these results, we developed a new measure of collaboration using electrodermal data: we computed the number of cycles between low and high synchronization. We found this measure to be significantly correlated with collaboration quality (r = 0.57) and learning gains (r = 0.47). This measure was not significantly correlated with the measures of physiological synchrony mentioned above, suggesting that it is capturing a different construct. We compare those results with prior studies and discuss implications for measuring collaborative process through physiological sensors.