What Do You Mean by Collaboration Analytics? A Conceptual Model

Martinez-Maldonado, R; Gašević, D; Echeverria, V; Fernandez Nieto, G; Swiecki, Z; Buckingham Shum, S

What Do You Mean by Collaboration Analytics? A Conceptual Model

Martinez-Maldonado, R Gašević, D Echeverria, V Fernandez Nieto, G Swiecki, Z Buckingham Shum, S

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Publisher:: Society for Learning Analytics Research
Publication Type:: Journal Article
Citation:: Journal of Learning Analytics, 2021, 8, (1), pp. 126-153
Issue Date:: 2021-04-09

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Field	Value	Language
dc.contributor.author	Martinez-Maldonado, R
dc.contributor.author	Gašević, D
dc.contributor.author	Echeverria, V
dc.contributor.author	Fernandez Nieto, G
dc.contributor.author	Swiecki, Z
dc.contributor.author	Buckingham Shum, S https://orcid.org/0000-0002-6334-7429
dc.date.accessioned	2022-09-17T07:21:02Z
dc.date.available	2022-09-17T07:21:02Z
dc.date.issued	2021-04-09
dc.identifier.citation	Journal of Learning Analytics, 2021, 8, (1), pp. 126-153
dc.identifier.issn	1929-7750
dc.identifier.issn	1929-7750
dc.identifier.uri	http://hdl.handle.net/10453/161872
dc.description.abstract	<jats:p>Using data to generate a deeper understanding of collaborative learning is not new, but automatically analyzing log data has enabled new means of identifying key indicators of effective collaboration and teamwork that can be used to predict outcomes and personalize feedback. Collaboration analytics is emerging as a new term to refer to computational methods for identifying salient aspects of collaboration from multiple group data sources for learners, educators, or other stakeholders to gain and act upon insights. Yet, it remains unclear how collaboration analytics go beyond previous work focused on modelling group interactions for the purpose of adapting instruction. This paper provides a conceptual model of collaboration analytics to help researchers and designers identify the opportunities enabled by such innovations to advance knowledge in, and provide enhanced support for, collaborative learning and teamwork. We argue that mapping from low-level data to higher-order constructs that are educationally meaningful, and that can be understood by educators and learners, is essential to assessing the validity of collaboration analytics. Through four cases, the paper illustrates the critical role of theory, task design, and human factors in the design of interfaces that inform actionable insights for improving collaboration and group learning.</jats:p>
dc.language	en
dc.publisher	Society for Learning Analytics Research
dc.relation.ispartof	Journal of Learning Analytics
dc.relation.isbasedon	10.18608/jla.2021.7227
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1303 Specialist Studies in Education
dc.title	What Do You Mean by Collaboration Analytics? A Conceptual Model
dc.type	Journal Article
utslib.citation.volume	8
utslib.for	1303 Specialist Studies in Education
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	open_access	*
dc.date.updated	2022-09-17T07:21:01Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	8
utslib.citation.issue	1

Abstract:

Using data to generate a deeper understanding of collaborative learning is not new, but automatically analyzing log data has enabled new means of identifying key indicators of effective collaboration and teamwork that can be used to predict outcomes and personalize feedback. Collaboration analytics is emerging as a new term to refer to computational methods for identifying salient aspects of collaboration from multiple group data sources for learners, educators, or other stakeholders to gain and act upon insights. Yet, it remains unclear how collaboration analytics go beyond previous work focused on modelling group interactions for the purpose of adapting instruction. This paper provides a conceptual model of collaboration analytics to help researchers and designers identify the opportunities enabled by such innovations to advance knowledge in, and provide enhanced support for, collaborative learning and teamwork. We argue that mapping from low-level data to higher-order constructs that are educationally meaningful, and that can be understood by educators and learners, is essential to assessing the validity of collaboration analytics. Through four cases, the paper illustrates the critical role of theory, task design, and human factors in the design of interfaces that inform actionable insights for improving collaboration and group learning.

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http://hdl.handle.net/10453/161872