Inferring Learning from Big Data: The Importance of a Transdisciplinary and Multidimensional Approach

Lodge, JM; Alhadad, SSJ; Lewis, MJ; Gašević, D

Inferring Learning from Big Data: The Importance of a Transdisciplinary and Multidimensional Approach

Lodge, JM Alhadad, SSJ Lewis, MJ Gašević, D

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Technology, Knowledge and Learning, 2017, 22, (3), pp. 385-400
Issue Date:: 2017-10-01

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Field	Value	Language
dc.contributor.author	Lodge, JM
dc.contributor.author	Alhadad, SSJ
dc.contributor.author	Lewis, MJ
dc.contributor.author	Gašević, D
dc.date.accessioned	2022-09-06T00:34:00Z
dc.date.available	2022-09-06T00:34:00Z
dc.date.issued	2017-10-01
dc.identifier.citation	Technology, Knowledge and Learning, 2017, 22, (3), pp. 385-400
dc.identifier.issn	2211-1670
dc.identifier.issn	2211-1670
dc.identifier.uri	http://hdl.handle.net/10453/161398
dc.description.abstract	The use of big data in higher education has evolved rapidly with a focus on the practical application of new tools and methods for supporting learning. In this paper, we depart from the core emphasis on application and delve into a mostly neglected aspect of the big data conversation in higher education. Drawing on developments in cognate disciplines, we analyse the inherent difficulties in inferring the complex phenomenon that is learning from big datasets. This forms the basis of a discussion about the possibilities for systematic collaboration across different paradigms and disciplinary backgrounds in interpreting big data for enhancing learning. The aim of this paper is to provide the foundation for a research agenda, where differing conceptualisations of learning become a strength in interpreting patterns in big datasets, rather than a point of contention.
dc.language	en
dc.publisher	Springer
dc.relation	http://purl.org/au-research/grants/arc/SR120300015
dc.relation.ispartof	Technology, Knowledge and Learning
dc.relation.isbasedon	10.1007/s10758-017-9330-3
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Inferring Learning from Big Data: The Importance of a Transdisciplinary and Multidimensional Approach
dc.type	Journal Article
utslib.citation.volume	22
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-09-06T00:33:59Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	22
utslib.citation.issue	3

Abstract:

The use of big data in higher education has evolved rapidly with a focus on the practical application of new tools and methods for supporting learning. In this paper, we depart from the core emphasis on application and delve into a mostly neglected aspect of the big data conversation in higher education. Drawing on developments in cognate disciplines, we analyse the inherent difficulties in inferring the complex phenomenon that is learning from big datasets. This forms the basis of a discussion about the possibilities for systematic collaboration across different paradigms and disciplinary backgrounds in interpreting big data for enhancing learning. The aim of this paper is to provide the foundation for a research agenda, where differing conceptualisations of learning become a strength in interpreting patterns in big datasets, rather than a point of contention.

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http://hdl.handle.net/10453/161398