Augmenting formative writing assessment with learning analytics: A design abstraction approach

Knight, S; Shibani, A; Shum, SB

Augmenting formative writing assessment with learning analytics: A design abstraction approach

Knight, S

Shibani, A

Shum, SB

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of International Conference of the Learning Sciences, ICLS, 2018, 3 (2018-June), pp. 1783 - 1790
Issue Date:: 2018-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Microsoft Word XML

Download Accepted Manuscript versionMicrosoft Word XML (870.33 kB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Knight, S https://orcid.org/0000-0002-8709-5780	en_US
dc.contributor.author	Shibani, A https://orcid.org/0000-0003-4619-8684	en_US
dc.contributor.author	Shum, SB https://orcid.org/0000-0002-6334-7429	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Proceedings of International Conference of the Learning Sciences, ICLS, 2018, 3 (2018-June), pp. 1783 - 1790	en_US
dc.identifier.issn	1814-9316	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130430
dc.description.abstract	© ISLS. There is increasing interest in use of learning analytics and technologies underpinned by artificial intelligence in the support of learning. In implementing and integrating these technologies there are challenges both with regard to developing the technologies themselves, and in aligning them with existing, or transformable, practices. In this paper we argue that by ‘augmenting’ formative tasks with learning analytics, we can achieve impact both through the integration of the tools, and through the support of existing good practices. We exemplify our approach through its application to the design of tasks for the key skill of learning how to write effectively. The development of these designs and their abstractions holds significant potential in bridging research and practice, by supporting the sharing and interrogation of designs in a way that is intimately tied to both practice (practical applied contexts), and research (through theorized and empirically supported designs targeting particular learning outcomes).	en_US
dc.relation.ispartof	Proceedings of International Conference of the Learning Sciences, ICLS	en_US
dc.title	Augmenting formative writing assessment with learning analytics: A design abstraction approach	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-June	en_US
utslib.citation.volume	3	en_US
utslib.for	130306 Educational Technology and Computing	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Transdisciplinary Innovation
utslib.copyright.status	open_access
pubs.issue	2018-June	en_US
pubs.publication-status	Published	en_US
pubs.volume	3	en_US

Abstract:

© ISLS. There is increasing interest in use of learning analytics and technologies underpinned by artificial intelligence in the support of learning. In implementing and integrating these technologies there are challenges both with regard to developing the technologies themselves, and in aligning them with existing, or transformable, practices. In this paper we argue that by ‘augmenting’ formative tasks with learning analytics, we can achieve impact both through the integration of the tools, and through the support of existing good practices. We exemplify our approach through its application to the design of tasks for the key skill of learning how to write effectively. The development of these designs and their abstractions holds significant potential in bridging research and practice, by supporting the sharing and interrogation of designs in a way that is intimately tied to both practice (practical applied contexts), and research (through theorized and empirically supported designs targeting particular learning outcomes).

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/130430