Learning to collaborate effectively requires practice, awareness of group dynamics, and reflection; often it benefits from coaching by an expert facilitator. However, in physical spaces it is not always easy to provide teams with evidence to support collaboration. Emerging technology provides a promising opportunity to make collocated collaboration visible by harnessing data about interactions and then mining and visualizing it. These collocated collaboration analytics can help researchers, designers, and users to understand the complexity of collaboration and to find ways they can support collaboration. This article introduces and motivates a set of principles for mining collocated collaboration data and draws attention to trade-offs that may need to be negotiated en route. We integrate Data Science principles and techniques with the advances in interactive surface devices and sensing technologies. We draw on a 7-year research program that has involved the analysis of six group situations in collocated settings with more than 500 users and a variety of surface technologies, tasks, grouping structures, and domains. The contribution of the article includes the key insights and themes that we have identified and summarized in a set of principles and dilemmas that can inform design of future collocated collaboration analytics innovations.

学习有效合作需要实践，对团队动态的意识和反思；通常，它得益于专家主持人的指导。但是，在物理空间中，向团队提供支持协作的证据并不总是那么容易。新兴技术通过利用有关交互的数据，然后对其进行挖掘和可视化，提供了使并置协作可见的机会。这些并置的协作分析可以帮助研究人员，设计人员和用户了解协作的复杂性，并找到支持协作的方式。本文介绍并激励了一组用于挖掘并置协作数据的原则，并提请注意可能在途中需要进行权衡的取舍。我们将数据科学原理和技术与交互式地面设备和传感技术的进步相结合。我们采用了一项为期7年的研究计划，该计划涉及在500多个用户和多种表面技术，任务，分组结构和领域的并置环境中对六个小组情况的分析。