Towards collaboration translucence: Giving meaning to multimodal group data
- Publisher:
- ACM
- Publication Type:
- Conference Proceeding
- Citation:
- CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019, pp. 1-16
- Issue Date:
- 2019-05-02
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 3290605.3300269.pdf | Published version | 2 MB |
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© 2019 Association for Computer Machinery. Collocated, face-to-face teamwork remains a pervasive mode of working, which is hard to replicate online. Team members’ embodied, multimodal interaction with each other and artefacts has been studied by researchers, but due to its complexity, has remained opaque to automated analysis. However, the ready availability of sensors makes it increasingly affordable to instrument work spaces to study teamwork and groupwork. The possibility of visualising key aspects of a collaboration has huge potential for both academic and professional learning, but a frontline challenge is the enrichment of quantitative data streams with the qualitative insights needed to make sense of them. In response, we introduce the concept of collaboration translucence, an approach to make visible selected features of group activity. This is grounded both theoretically (in the physical, epistemic, social and affective dimensions of group activity), and contextually (using domain-specific concepts). We illustrate the approach from the automated analysis of healthcare simulations to train nurses, generating four visual proxies that fuse multimodal data into higher order patterns.
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