"Belonging Analytics": A Proposal

Buckingham Shum, S; Lim, L-A; Felten, P; Uno, J

"Belonging Analytics": A Proposal

Buckingham Shum, S

Lim, L-A Felten, P Uno, J

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Publication Type:: Journal Article
Citation:: Learning Letters, 2023, 1, pp. 1-12
Issue Date:: 2023-08-13

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Field	Value	Language
dc.contributor.author	Buckingham Shum, S https://orcid.org/0000-0002-6334-7429
dc.contributor.author	Lim, L-A
dc.contributor.author	Felten, P
dc.contributor.author	Uno, J
dc.date.accessioned	2023-09-17T11:16:14Z
dc.date.available	2023-09-17T11:16:14Z
dc.date.issued	2023-08-13
dc.identifier.citation	Learning Letters, 2023, 1, pp. 1-12
dc.identifier.uri	http://hdl.handle.net/10453/172143
dc.description.abstract	Students’ sense of belonging is associated with successful transition into higher education and a range of positive outcomes including enhanced learning, well-being, and demonstrated achievement. The COVID-19 pandemic underscored the importance of belonging as the shift to online learning highlighted the challenges of supporting and monitoring student belonging. Attending to belonging is not simple, however; students’ experiences with belonging are complex, dynamic, and contextual. In creating a new agenda connecting the fields of belonging and learning analytics, we propose the idea of “belonging analytics” to address the challenge of supporting and tracking students’ belonging. In this paper, we discuss how the understanding of belonging may be enhanced through learning analytics. Advancements in learning analytics, such as digital trace data, narratives, textual data, or a combination, may be harnessed to gain insights into ongoing experience of belonging, and consequently to support belonging. We conclude with a set of open questions to interested researchers and practitioners, to advance the field of belonging analytics.
dc.language	en
dc.relation.ispartof	Learning Letters
dc.relation.isbasedon	10.59453/EAXA8005
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.rights	This work is licensed under CC BY-ND 4.0.
dc.title	"Belonging Analytics": A Proposal
dc.type	Journal Article
utslib.citation.volume	1
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
pubs.organisational-group	/University of Technology Sydney/Strength - CREDS - Centre for Research on Education in a Digital Society
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2023-09-17T11:16:14Z
pubs.publication-status	Published
pubs.volume	1

Abstract:

Students’ sense of belonging is associated with successful transition into higher education and a range of positive outcomes including enhanced learning, well-being, and demonstrated achievement. The COVID-19 pandemic underscored the importance of belonging as the shift to online learning highlighted the challenges of supporting and monitoring student belonging. Attending to belonging is not simple, however; students’ experiences with belonging are complex, dynamic, and contextual. In creating a new agenda connecting the fields of belonging and learning analytics, we propose the idea of “belonging analytics” to address the challenge of supporting and tracking students’ belonging. In this paper, we discuss how the understanding of belonging may be enhanced through learning analytics. Advancements in learning analytics, such as digital trace data, narratives, textual data, or a combination, may be harnessed to gain insights into ongoing experience of belonging, and consequently to support belonging. We conclude with a set of open questions to interested researchers and practitioners, to advance the field of belonging analytics.

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http://hdl.handle.net/10453/172143