Learning Analytics Impact: Critical Conversations on Relevance and Social Responsibility

Ochoa, X; Knight, S; Wise, AF

Learning Analytics Impact: Critical Conversations on Relevance and Social Responsibility

Ochoa, X Knight, S

Wise, AF

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Publisher:: Society for Learning Analytics Research
Publication Type:: Journal Article
Citation:: Journal of Learning Analytics, 2020, 7, (3), pp. 1-5
Issue Date:: 2020-12-17

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Field	Value	Language
dc.contributor.author	Ochoa, X
dc.contributor.author	Knight, S https://orcid.org/0000-0002-8709-5780
dc.contributor.author	Wise, AF
dc.date.accessioned	2021-01-04T00:10:51Z
dc.date.available	2021-01-04T00:10:51Z
dc.date.issued	2020-12-17
dc.identifier.citation	Journal of Learning Analytics, 2020, 7, (3), pp. 1-5
dc.identifier.issn	1929-7750
dc.identifier.issn	1929-7750
dc.identifier.uri	http://hdl.handle.net/10453/145062
dc.description.abstract	<jats:p>Our 2019 editorial opened a dialogue about what is needed to foster an impactful field of learning analytics (Knight, Wise, & Ochoa, 2019). As we head toward the close of a tumultuous year that has raised profound questions about the structure and processes of formal education and its role in society, this conversation is more relevant than ever. That editorial, and a recent online community event, focused on one component of the impact: standards for scientific rigour and the criteria by which knowledge claims in an interdisciplinary, multi-methodology field should be judged. These initial conversations revealed important commonalities across statistical, computational, and qualitative approaches in terms of a need for greater explanation and justification of choices in using appropriate data, models, or other methodological approaches, as well as the many micro-decisions made in applying specific methodologies to specific studies. The conversations also emphasize the need to perform different checks (for overfitting, for bias, for replicability, for the contextual bounds of applicability, for disconfirming cases) and the importance of learning analytics research being relevant by situating itself within a set of educational values, making tighter connections to theory, and considering its practical mobilization to affect learning. These ideas will serve as the starting point for a series of detailed follow-up conversations across the community, with the goal of generating updated standards and guidance for JLA articles.</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.2020.73.1
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1303 Specialist Studies in Education
dc.title	Learning Analytics Impact: Critical Conversations on Relevance and Social Responsibility
dc.type	Journal Article
utslib.citation.volume	7
utslib.for	1303 Specialist Studies in Education
pubs.organisational-group	/University of Technology Sydney/Faculty of Transdisciplinary Innovation
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2021-01-04T00:10:45Z
pubs.issue	3
pubs.publication-status	Published online
pubs.volume	7
utslib.citation.issue	3

Abstract:

Our 2019 editorial opened a dialogue about what is needed to foster an impactful field of learning analytics (Knight, Wise, & Ochoa, 2019). As we head toward the close of a tumultuous year that has raised profound questions about the structure and processes of formal education and its role in society, this conversation is more relevant than ever. That editorial, and a recent online community event, focused on one component of the impact: standards for scientific rigour and the criteria by which knowledge claims in an interdisciplinary, multi-methodology field should be judged. These initial conversations revealed important commonalities across statistical, computational, and qualitative approaches in terms of a need for greater explanation and justification of choices in using appropriate data, models, or other methodological approaches, as well as the many micro-decisions made in applying specific methodologies to specific studies. The conversations also emphasize the need to perform different checks (for overfitting, for bias, for replicability, for the contextual bounds of applicability, for disconfirming cases) and the importance of learning analytics research being relevant by situating itself within a set of educational values, making tighter connections to theory, and considering its practical mobilization to affect learning. These ideas will serve as the starting point for a series of detailed follow-up conversations across the community, with the goal of generating updated standards and guidance for JLA articles.

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http://hdl.handle.net/10453/145062