Applying Machine Translation and Language Modelling Strategies for the Recommendation Task of Micro Learning Service

Lin, J; Sun, G; Beydoun, G; Li, L

Applying Machine Translation and Language Modelling Strategies for the Recommendation Task of Micro Learning Service

Lin, J Sun, G Beydoun, G

Li, L

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Publisher:: INT FORUM EDUCATIONAL TECHNOLOGY & SOC-IFETS
Publication Type:: Journal Article
Citation:: Educational Technology and Society, 2022, 25, (1), pp. 205-212
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Lin, J
dc.contributor.author	Sun, G
dc.contributor.author	Beydoun, G https://orcid.org/0000-0001-8087-5445
dc.contributor.author	Li, L
dc.date.accessioned	2022-05-25T06:34:48Z
dc.date.available	2022-05-25T06:34:48Z
dc.date.issued	2022-01-01
dc.identifier.citation	Educational Technology and Society, 2022, 25, (1), pp. 205-212
dc.identifier.issn	1176-3647
dc.identifier.issn	1436-4522
dc.identifier.uri	http://hdl.handle.net/10453/157688
dc.description.abstract	A newly emerged micro learning service offers a flexible formal, informal, or non-formal online learning opportunity to worldwide users with different backgrounds in real-time. With the assist of big data technology and cloud computing service, online learners can access tremendous fine-grained learning resources through micro learning service. However, big data also causes serious information overload during online learning activities. Hence, an intelligent recommender system is required to filter out not-suitable learning resources and pick the one that matches the learner’s learning requirement and academic background. From the perspective of natural language processing (NLP), this study proposed a novel recommender system that utilises machine translation and language modelling. The proposed model aims to overcome the defects of conventional recommender systems and further enhance distinguish ability of the recommender system for different learning resources.
dc.language	English
dc.publisher	INT FORUM EDUCATIONAL TECHNOLOGY & SOC-IFETS
dc.relation.ispartof	Educational Technology and Society
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0806 Information Systems, 1303 Specialist Studies in Education
dc.subject.classification	Education
dc.title	Applying Machine Translation and Language Modelling Strategies for the Recommendation Task of Micro Learning Service
dc.type	Journal Article
utslib.citation.volume	25
utslib.for	0806 Information Systems
utslib.for	1303 Specialist Studies in Education
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/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2022-05-25T06:34:46Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	25
utslib.citation.issue	1

Abstract:

A newly emerged micro learning service offers a flexible formal, informal, or non-formal online learning opportunity to worldwide users with different backgrounds in real-time. With the assist of big data technology and cloud computing service, online learners can access tremendous fine-grained learning resources through micro learning service. However, big data also causes serious information overload during online learning activities. Hence, an intelligent recommender system is required to filter out not-suitable learning resources and pick the one that matches the learner’s learning requirement and academic background. From the perspective of natural language processing (NLP), this study proposed a novel recommender system that utilises machine translation and language modelling. The proposed model aims to overcome the defects of conventional recommender systems and further enhance distinguish ability of the recommender system for different learning resources.

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http://hdl.handle.net/10453/157688