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.

中文翻译：

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揭示生理同步和协作学习的现有和新措施之间的关系：混合方法研究

在过去的十年中，人们开始重新使用新的数据收集工具来捕捉二十一世纪的技能。在本文中，我们利用现有的数据集，其中使用皮肤电活动（EDA）来确定生产合作的标志。从42双参与者（数据来Ñ = 84）没有编码经验，并被要求对机器人编程以解决各种迷宫。因为对生理同步与协作学习之间的关系知之甚少，所以我们探索了四种不同的同步度量：信号匹配（SM），瞬时导数匹配（IDM），方向性协议（DA）和皮尔逊相关（PC）。总体而言，我们发现PC与学习收益（r = 0.35）和DA与协作质量（r = 0.3）正相关。为了进一步了解这些结果，我们还定性分析了两组并确定了生理同步性高或低的情况。当生产性小组的成员对外部事件做出反应时（例如，遵循指示，收到提示），我们观察到较高的同步值，他们在观看视频或彼此互动时会产生振荡，而在编程时和/或似乎感到困惑时会降低数值。基于这些结果，我们开发了一种使用皮肤电数据的协作新方法：我们计算了低同步和高同步之间的循环数。我们发现这项措施与协作质量（r = 0.57）和学习收益（r = 0.47）显着相关。此措施与上述生理同步措施没有显着相关，表明它正在捕获不同的构建体。我们将这些结果与先前的研究进行比较，并讨论通过生理传感器测量协作过程的意义。我们使用皮肤电动数据开发了一种新的协作度量：我们计算了低同步和高同步之间的循环数。我们发现这项措施与协作质量（r = 0.57）和学习收益（r = 0.47）显着相关。此措施与上述生理同步措施没有显着相关，表明它正在捕获不同的构建体。我们将这些结果与先前的研究进行比较，并讨论通过生理传感器测量协作过程的意义。我们使用皮肤电动数据开发了一种新的协作度量：我们计算了低同步和高同步之间的循环数。我们发现这项措施与协作质量（r = 0.57）和学习收益（r = 0.47）显着相关。此措施与上述生理同步措施没有显着相关，表明它正在捕获不同的构建体。我们将这些结果与先前的研究进行比较，并讨论通过生理传感器测量协作过程的意义。此措施与上述生理同步措施没有显着相关，表明它正在捕获不同的构建体。我们将这些结果与先前的研究进行比较，并讨论通过生理传感器测量协作过程的意义。此措施与上述生理同步措施没有显着相关，表明它正在捕获不同的构建体。我们将这些结果与先前的研究进行比较，并讨论通过生理传感器测量协作过程的意义。