Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction Data

Martinez-Maldonado, R; Kay, J; Buckingham Shum, S; Yacef, K

Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction Data

Martinez-Maldonado, R

Kay, J Buckingham Shum, S

Yacef, K

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Publication Type:: Journal Article
Citation:: Human-Computer Interaction, 2019, 34 (1), pp. 1 - 50
Issue Date:: 2019-01-02

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Field	Value	Language
dc.contributor.author	Martinez-Maldonado, R https://orcid.org/0000-0002-8375-1816	en_US
dc.contributor.author	Kay, J	en_US
dc.contributor.author	Buckingham Shum, S https://orcid.org/0000-0002-6334-7429	en_US
dc.contributor.author	Yacef, K	en_US
dc.date.available	2020-05-25T19:04:12Z
dc.date.issued	2019-01-02	en_US
dc.identifier.citation	Human-Computer Interaction, 2019, 34 (1), pp. 1 - 50	en_US
dc.identifier.issn	0737-0024	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117172
dc.description.abstract	© 2019, Copyright © 2017 Taylor & Francis Group, LLC. 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.	en_US
dc.relation.ispartof	Human-Computer Interaction	en_US
dc.relation.isbasedon	10.1080/07370024.2017.1338956	en_US
dc.subject.classification	Human Factors	en_US
dc.title	Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction Data	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	34	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1202 Building	en_US
utslib.for	1701 Psychology	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	12 Built Environment and Design	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
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/DVC (Teaching and Learning)/Connected Intelligence Centre
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	34	en_US

Abstract:

© 2019, Copyright © 2017 Taylor & Francis Group, LLC. 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.

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http://hdl.handle.net/10453/117172