Collocated collaboration analytics: principles and dilemmas for mining multimodal interaction data

Roberto Martinez-Maldonado, Judy Kay, Simon Buckingham Shum, Kalina Yacef

Research output: Contribution to journalArticleResearchpeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1-50
Number of pages50
JournalHuman-Computer Interaction
Issue number1
Publication statusPublished - 2019
Externally publishedYes

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