Discovering Time Management Strategies in Learning Processes Using Process Mining Techniques

Ahmad Uzir, N; Gašević, D; Matcha, W; Jovanović, J; Pardo, A; Lim, LA; Gentili, S

Discovering Time Management Strategies in Learning Processes Using Process Mining Techniques

Ahmad Uzir, N Gašević, D Matcha, W Jovanović, J Pardo, A Lim, LA Gentili, S

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Publisher:: Springer
Publication Type:: Chapter
Citation:: Transforming Learning with Meaningful Technologies, 2019, 11722 LNCS, pp. 555-569
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Ahmad Uzir, N
dc.contributor.author	Gašević, D
dc.contributor.author	Matcha, W
dc.contributor.author	Jovanović, J
dc.contributor.author	Pardo, A
dc.contributor.author	Lim, LA
dc.contributor.author	Gentili, S
dc.contributor.editor	Scheffel, M
dc.contributor.editor	Broisin, J
dc.contributor.editor	PammerSchindler, V
dc.contributor.editor	Ioannou, A
dc.contributor.editor	Schneider, J
dc.date.accessioned	2023-01-04T02:16:02Z
dc.date.available	2023-01-04T02:16:02Z
dc.date.issued	2019-01-01
dc.identifier.citation	Transforming Learning with Meaningful Technologies, 2019, 11722 LNCS, pp. 555-569
dc.identifier.isbn	9783030297350
dc.identifier.uri	http://hdl.handle.net/10453/164669
dc.description.abstract	This paper reports the findings of a study that proposed a novel learning analytic methodology that combines process mining with cluster analysis to study time management in the context of blended and online learning. The study was conducted with first-year students (N = 241) who were enrolled in blended learning of a health science course. The study identified four distinct time management tactics and three strategies. The tactics and strategies were interpreted according to the established theoretical framework of self-regulated learning in terms of student decisions about what to study, how long to study, and how to study. The study also identified significant differences in academic performance among students who followed different time management strategies.
dc.format.extent	86
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Transforming Learning with Meaningful Technologies
dc.relation.ispartofseries	Lecture Notes in Computer Science
dc.relation.isbasedon	10.1007/978-3-030-29736-7_41
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Discovering Time Management Strategies in Learning Processes Using Process Mining Techniques
dc.type	Chapter
utslib.citation.volume	11722 LNCS
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	2023-01-04T02:16:00Z
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.volume	11722 LNCS
dc.location	Switzerland

Abstract:

This paper reports the findings of a study that proposed a novel learning analytic methodology that combines process mining with cluster analysis to study time management in the context of blended and online learning. The study was conducted with first-year students (N = 241) who were enrolled in blended learning of a health science course. The study identified four distinct time management tactics and three strategies. The tactics and strategies were interpreted according to the established theoretical framework of self-regulated learning in terms of student decisions about what to study, how long to study, and how to study. The study also identified significant differences in academic performance among students who followed different time management strategies.

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http://hdl.handle.net/10453/164669