Towards skills-based curriculum analytics: Can we automate the recognition of prior learning?

Kitto, K; Sarathy, N; Gromov, A; Liu, M; Musial, K; Shum, SB

Towards skills-based curriculum analytics: Can we automate the recognition of prior learning?

Kitto, K

Sarathy, N Gromov, A

Liu, M

Musial, K Shum, SB

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Publisher:: ACM
Publication Type:: Conference Proceeding
Citation:: ACM International Conference Proceeding Series, 2020, pp. 171-180
Issue Date:: 2020-03-23

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Field	Value	Language
dc.contributor.author	Kitto, K https://orcid.org/0000-0001-7642-7121
dc.contributor.author	Sarathy, N
dc.contributor.author	Gromov, A https://orcid.org/0000-0001-9828-837X
dc.contributor.author	Liu, M https://orcid.org/0000-0003-4256-6531
dc.contributor.author	Musial, K
dc.contributor.author	Shum, SB
dc.date.accessioned	2020-06-29T21:08:01Z
dc.date.available	2020-06-29T21:08:01Z
dc.date.issued	2020-03-23
dc.identifier.citation	ACM International Conference Proceeding Series, 2020, pp. 171-180
dc.identifier.isbn	9781450377126
dc.identifier.uri	http://hdl.handle.net/10453/141691
dc.description.abstract	© 2020 Association for Computing Machinery. In an era that will increasingly depend upon lifelong learning, the LA community will need to facilitate the movement and sharing of data and information across institutional and geographic boundaries. This will help us to recognise prior learning (RPL) and to personalise the learner experience. Here, we explore the utility of skills-based curriculum analytics and how it might facilitate the process of awarding RPL between two institutions. We explore the potential utility of combining natural language processing and skills taxonomies to map between subject descriptions for these two different institutions, presenting two algorithms we have developed to facilitate RPL and evaluating their performance. We draw attention to some of the issues that arise, listing areas that we consider ripe for future work in a surprisingly underexplored area. © 2020 Copyright held by the owner/author(s).
dc.language	en
dc.publisher	ACM
dc.relation.ispartof	ACM International Conference Proceeding Series
dc.relation.ispartof	LAK '20: 10th International Conference on Learning Analytics and Knowledge
dc.relation.isbasedon	10.1145/3375462.3375526
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Towards skills-based curriculum analytics: Can we automate the recognition of prior learning?
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)/Connected Intelligence Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2020-06-29T21:07:57Z
pubs.publication-status	Published

Abstract:

© 2020 Association for Computing Machinery. In an era that will increasingly depend upon lifelong learning, the LA community will need to facilitate the movement and sharing of data and information across institutional and geographic boundaries. This will help us to recognise prior learning (RPL) and to personalise the learner experience. Here, we explore the utility of skills-based curriculum analytics and how it might facilitate the process of awarding RPL between two institutions. We explore the potential utility of combining natural language processing and skills taxonomies to map between subject descriptions for these two different institutions, presenting two algorithms we have developed to facilitate RPL and evaluating their performance. We draw attention to some of the issues that arise, listing areas that we consider ripe for future work in a surprisingly underexplored area. © 2020 Copyright held by the owner/author(s).

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http://hdl.handle.net/10453/141691