HuCETA: Human-Centered Embodied Teamwork Analytics
After about 7 years working on multimodal teamwork analytics (specifically in nursing simulations) with Roberto Martinez-Maldonado, and then PhDs with Vanessa Echeverria & Gloria Fernandez — and more recently in an ARC-funded project with Dragan Gasevic, Lixiang (Jimmie) Yan, Linxuan Zhao — we are moving towards theoretically grounded, open source infrastructure for analysing collocated teamwork. We’ve distilled the essence of all that we’ve learnt into a new conceptual framework called HuCETA:
Echeverria, V., Martinez-Maldonado, R., Yan, L., Zhao, L., Fernandez-Nieto, G., Gasevic, D., & Buckingham Shum, S. (2022). HuCETA: A Framework for Human-Centered Embodied Teamwork Analytics. IEEE Pervasive Computing, 1-11. https://doi.org/10.1109/MPRV.2022.3217454 [Open Access Eprint]
Abstract: Collocated teamwork remains a pervasive practice across all professional sectors. Even though live observations and video analysis have been utilized for understanding embodied interaction of team members, these approaches are impractical for scaling up the provision of feedback that can promote developing high-performance teamwork skills. Enriching spaces with sensors capable of automatically capturing team activity data can improve learning and reflection. Yet, connecting the enormous amounts of data such sensors can generate with constructs related to teamwork remains challenging. This article presents a framework to support the development of human-centered embodied teamwork analytics by 1) enabling hybrid human–machine multimodal sensing; 2) embedding educators’ and experts’ knowledge into computational team models; and 3) generating human-driven data storytelling interfaces for reflection and decision making. This is illustrated through an in-the-wild study in the context of healthcare simulation, where predictive modeling, epistemic network analysis, and data storytelling are used to support educators and nursing teams.
