Evolutionary learner profile optimization using rare and negative association rules for micro open learning

Sun, G; Lin, J; Shen, J; Cui, T; Xu, D; Chen, H

Evolutionary learner profile optimization using rare and negative association rules for micro open learning

Sun, G Lin, J Shen, J Cui, T Xu, D Chen, H

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Publisher:: Springer International Publishing
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, 12149 LNCS, pp. 432-440
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Sun, G
dc.contributor.author	Lin, J
dc.contributor.author	Shen, J
dc.contributor.author	Cui, T
dc.contributor.author	Xu, D
dc.contributor.author	Chen, H
dc.date.accessioned	2021-05-24T05:09:40Z
dc.date.available	2021-05-24T05:09:40Z
dc.date.issued	2020-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, 12149 LNCS, pp. 432-440
dc.identifier.isbn	9783030496623
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/149146
dc.description.abstract	The actual data availability, readiness and publicity has slowed down the research of making use of computational intelligence to improve the knowledge delivery in an emerging learning mode, namely adaptive micro open learning, which naturally has high demand in quality and quantity of data to be fed. In this study, we contribute a novel approach to tackle the current scarcity of both data and rules in micro open learning, by adopting evolutionary algorithm to produce association rules with both rare and negative associations taken into account. These rules further drive the generation and optimization of learner profiles through refinement and augmentation, in order to maintain them in a low-dimensional, descriptive and interpretable form.
dc.language	en
dc.publisher	Springer International Publishing
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-030-49663-0_54
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Evolutionary learner profile optimization using rare and negative association rules for micro open learning
dc.type	Conference Proceeding
utslib.citation.volume	12149 LNCS
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	closed_access	*
dc.date.updated	2021-05-24T05:09:39Z
pubs.publication-status	Published
pubs.volume	12149 LNCS

Abstract:

The actual data availability, readiness and publicity has slowed down the research of making use of computational intelligence to improve the knowledge delivery in an emerging learning mode, namely adaptive micro open learning, which naturally has high demand in quality and quantity of data to be fed. In this study, we contribute a novel approach to tackle the current scarcity of both data and rules in micro open learning, by adopting evolutionary algorithm to produce association rules with both rare and negative associations taken into account. These rules further drive the generation and optimization of learner profiles through refinement and augmentation, in order to maintain them in a low-dimensional, descriptive and interpretable form.

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http://hdl.handle.net/10453/149146