Augmenting Assessment with Learning Analytics

Knight, S

Augmenting Assessment with Learning Analytics

Knight, S

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: Re-imagining University Assessment in a Digital World, 2020
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Knight, S https://orcid.org/0000-0002-8709-5780
dc.contributor.editor	Bearman, M
dc.contributor.editor	Dawson, P
dc.contributor.editor	Ajjawi, R
dc.contributor.editor	Tai, J
dc.contributor.editor	Boud, D
dc.date.accessioned	2020-07-16T01:16:13Z
dc.date.available	2020-07-16T01:16:13Z
dc.date.issued	2020
dc.identifier.citation	Re-imagining University Assessment in a Digital World, 2020
dc.identifier.isbn	3030419568
dc.identifier.isbn	9783030419561
dc.identifier.uri	http://hdl.handle.net/10453/141784
dc.description.abstract	Learning analytics as currently deployed has tended to consist of large-scale analyses of available learning process data to provide descriptive or predictive insight into behaviours. What is sometimes missing in this analysis is a connection to human-interpretable, actionable, diagnostic information. To gain traction, learning analytics researchers should work within existing good practice particularly in assessment, where high quality assessments are designed to provide both student and educator with diagnostic or formative feedback. Such a model keeps the human in the analytics design and implementation loop, by supporting student, peer, tutor, and instructor sense-making of assessment data, while adding value from computational analyses.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Re-imagining University Assessment in a Digital World
dc.relation.isbasedon	https://doi.org/10.1007/978-3-030-41956-1_10	en_us
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in Re-imagining University Assessment in a Digital World. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-41956-1_10.	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Augmenting Assessment with Learning Analytics
dc.type	Chapter
pubs.organisational-group	/University of Technology Sydney/Faculty of Transdisciplinary Innovation
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.date.updated	2020-07-16T01:16:08Z
pubs.publication-status	Published

Abstract:

Learning analytics as currently deployed has tended to consist of large-scale analyses of available learning process data to provide descriptive or predictive insight into behaviours. What is sometimes missing in this analysis is a connection to human-interpretable, actionable, diagnostic information. To gain traction, learning analytics researchers should work within existing good practice particularly in assessment, where high quality assessments are designed to provide both student and educator with diagnostic or formative feedback. Such a model keeps the human in the analytics design and implementation loop, by supporting student, peer, tutor, and instructor sense-making of assessment data, while adding value from computational analyses.

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http://hdl.handle.net/10453/141784