Ontological Learner Profile Identification for Cold Start Problem in Micro Learning Resources Delivery

Sun, G; Cui, T; Shen, J; Xu, D; Beydoun, G; Chen, S

Ontological Learner Profile Identification for Cold Start Problem in Micro Learning Resources Delivery

Sun, G Cui, T Shen, J

Xu, D Beydoun, G

Chen, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017, 2017, pp. 16 - 20
Issue Date:: 2017-08-03

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Field	Value	Language
dc.contributor.author	Sun, G	en_US
dc.contributor.author	Cui, T	en_US
dc.contributor.author	Shen, J https://orcid.org/0000-0002-9403-7140	en_US
dc.contributor.author	Xu, D	en_US
dc.contributor.author	Beydoun, G https://orcid.org/0000-0001-8087-5445	en_US
dc.contributor.author	Chen, S	en_US
dc.date.issued	2017-08-03	en_US
dc.identifier.citation	Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017, 2017, pp. 16 - 20	en_US
dc.identifier.isbn	9781538638705	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126835
dc.description.abstract	© 2017 IEEE. Open learning is a rising trend in the educational sector and it attracts millions of learners to be engaged to enjoy massive latest and free open education resources (OERs). Through the use of mobile devices, open learning is often carried out in a micro learning mode, where each unit of learning activity is commonly shorter than 15 minutes. Learners are often at a loss in the process of choosing OER leading to their long term objectives and short term demands. Our pilot work, namely MLaaS, proposed a smart system to deliver personalized OER with micro learning to satisfy their real-time needs, while its decision-making process is scarcely supported due to the lack of historical data. Inspired by this, MLaaS now embeds a new solution to tackle the cold start problem, by opening up a brand new profile for each learner and delivering them the first resources in their fresh start learning journey. In this paper, we also propose an ontology-based mechanism for learning prediction and recommendation.	en_US
dc.relation.ispartof	Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017	en_US
dc.relation.isbasedon	10.1109/ICALT.2017.22	en_US
dc.title	Ontological Learner Profile Identification for Cold Start Problem in Micro Learning Resources Delivery	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
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
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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. Open learning is a rising trend in the educational sector and it attracts millions of learners to be engaged to enjoy massive latest and free open education resources (OERs). Through the use of mobile devices, open learning is often carried out in a micro learning mode, where each unit of learning activity is commonly shorter than 15 minutes. Learners are often at a loss in the process of choosing OER leading to their long term objectives and short term demands. Our pilot work, namely MLaaS, proposed a smart system to deliver personalized OER with micro learning to satisfy their real-time needs, while its decision-making process is scarcely supported due to the lack of historical data. Inspired by this, MLaaS now embeds a new solution to tackle the cold start problem, by opening up a brand new profile for each learner and delivering them the first resources in their fresh start learning journey. In this paper, we also propose an ontology-based mechanism for learning prediction and recommendation.

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http://hdl.handle.net/10453/126835