HuCETA: A Framework for Human-Centered Embodied Teamwork Analytics

Echeverria, V; Martinez-Maldonado, R; Yan, L; Zhao, L; Fernandez-Nieto, G; Gasevic, D; Shum, SB

HuCETA: A Framework for Human-Centered Embodied Teamwork Analytics

Echeverria, V Martinez-Maldonado, R Yan, L Zhao, L Fernandez-Nieto, G Gasevic, D Shum, SB

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Publisher:: IEEE COMPUTER SOC
Publication Type:: Journal Article
Citation:: IEEE Pervasive Computing, 2023, 22, (1), pp. 39-49
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Echeverria, V
dc.contributor.author	Martinez-Maldonado, R
dc.contributor.author	Yan, L
dc.contributor.author	Zhao, L
dc.contributor.author	Fernandez-Nieto, G
dc.contributor.author	Gasevic, D
dc.contributor.author	Shum, SB
dc.date.accessioned	2024-03-14T21:41:11Z
dc.date.available	2024-03-14T21:41:11Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Pervasive Computing, 2023, 22, (1), pp. 39-49
dc.identifier.issn	1536-1268
dc.identifier.issn	1558-2590
dc.identifier.uri	http://hdl.handle.net/10453/176724
dc.description.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.
dc.language	English
dc.publisher	IEEE COMPUTER SOC
dc.relation	http://purl.org/au-research/grants/arc/DP210100060
dc.relation.ispartof	IEEE Pervasive Computing
dc.relation.isbasedon	10.1109/MPRV.2022.3217454
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0805 Distributed Computing, 0806 Information Systems, 0906 Electrical and Electronic Engineering
dc.subject.classification	Distributed Computing
dc.subject.classification	4606 Distributed computing and systems software
dc.title	HuCETA: A Framework for Human-Centered Embodied Teamwork Analytics
dc.type	Journal Article
utslib.citation.volume	22
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	University of Technology Sydney/Strength - CREDS - Centre for Research on Education in a Digital Society
utslib.copyright.status	in_progress	*
dc.date.updated	2024-03-14T21:41:09Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	22
utslib.citation.issue	1

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/176724