Learning Analytics in the Corporate Sector: What Business Leaders Say

Poquet, O; Lim, LA; De Laat, M

Learning Analytics in the Corporate Sector: What Business Leaders Say

Poquet, O Lim, LA De Laat, M

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Learning Technologies, 2022, 15, (5), pp. 605-619
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Poquet, O
dc.contributor.author	Lim, LA
dc.contributor.author	De Laat, M
dc.date.accessioned	2022-11-23T03:43:37Z
dc.date.available	2022-11-23T03:43:37Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Learning Technologies, 2022, 15, (5), pp. 605-619
dc.identifier.issn	1939-1382
dc.identifier.issn	1939-1382
dc.identifier.uri	http://hdl.handle.net/10453/163679
dc.description.abstract	The widespread adoption of digital tools for learning enables extensive data collection about employee learning activities. This study analyzes how corporations use data to inform and support their learning needs. This study presents a qualitative analysis of what managers and executives overseeing learning and development initiatives in their respective organizations value in learning analytics (LA) at workplace. The interviews were analyzed through a framework that combined lenses of value-sensitive design and value creation to uncover the values underpinning current applications of LA in the corporate sector. We found that corporations vary in their strategic LA orientations when it comes to organizational learning and development. However, although aspirational narratives of executives are diverse, their activities are not. The implications of the rich qualitative findings suggest that learning sciences can support organizational learning by creating diagnostic tools to identify organizational orientations for learning analytics. Computer scientists can also support decision making and research on corporate learning analytics by creating tools that can help map organizational aspirations and activities in relation to the various data sources, stemming from different organizational units. We conclude outlining these recommendations.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Learning Technologies
dc.relation.isbasedon	10.1109/TLT.2022.3164783
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0806 Information Systems, 1005 Communications Technologies, 1303 Specialist Studies in Education
dc.title	Learning Analytics in the Corporate Sector: What Business Leaders Say
dc.type	Journal Article
utslib.citation.volume	15
utslib.for	0806 Information Systems
utslib.for	1005 Communications Technologies
utslib.for	1303 Specialist Studies in Education
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	2022-11-23T03:43:36Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	5

Abstract:

The widespread adoption of digital tools for learning enables extensive data collection about employee learning activities. This study analyzes how corporations use data to inform and support their learning needs. This study presents a qualitative analysis of what managers and executives overseeing learning and development initiatives in their respective organizations value in learning analytics (LA) at workplace. The interviews were analyzed through a framework that combined lenses of value-sensitive design and value creation to uncover the values underpinning current applications of LA in the corporate sector. We found that corporations vary in their strategic LA orientations when it comes to organizational learning and development. However, although aspirational narratives of executives are diverse, their activities are not. The implications of the rich qualitative findings suggest that learning sciences can support organizational learning by creating diagnostic tools to identify organizational orientations for learning analytics. Computer scientists can also support decision making and research on corporate learning analytics by creating tools that can help map organizational aspirations and activities in relation to the various data sources, stemming from different organizational units. We conclude outlining these recommendations.

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

http://hdl.handle.net/10453/163679