(WIP) Evaluation of a Cloud-Based System for Delivering Adaptive Micro Open Education Resource to Fresh Learners

Sun, G; Cui, T; Dong, F; Xu, D; Shen, J; Chen, S; Lin, J

(WIP) Evaluation of a Cloud-Based System for Delivering Adaptive Micro Open Education Resource to Fresh Learners

Sun, G Cui, T Dong, F Xu, D Shen, J

Chen, S Lin, J

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Cloud Computing, CLOUD, 2018, 2018-July pp. 586 - 589
Issue Date:: 2018-09-07

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Field	Value	Language
dc.contributor.author	Sun, G	en_US
dc.contributor.author	Cui, T	en_US
dc.contributor.author	Dong, F	en_US
dc.contributor.author	Xu, D	en_US
dc.contributor.author	Shen, J https://orcid.org/0000-0002-9403-7140	en_US
dc.contributor.author	Chen, S	en_US
dc.contributor.author	Lin, J	en_US
dc.date.issued	2018-09-07	en_US
dc.identifier.citation	IEEE International Conference on Cloud Computing, CLOUD, 2018, 2018-July pp. 586 - 589	en_US
dc.identifier.isbn	9781538672358	en_US
dc.identifier.issn	2159-6182	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130256
dc.description.abstract	© 2018 IEEE. In this paper, we present an online computation approach implemented in a cloud-based system to assist open education resource (OER) providers and instructors dealing with the sparsity of data in micro OER recommendation. An algorithmic framework is provided to realize the novel micro OER recommendation system based on heuristic rules. These rules can also optimize the approaches to blending new-coming micro OERs into established learning paths. Comparing with different widely used recommender systems, our evaluation shows the proposed heuristic algorithms for online computation performs satisfactorily in terms of precision and recall values.	en_US
dc.relation.ispartof	IEEE International Conference on Cloud Computing, CLOUD	en_US
dc.relation.isbasedon	10.1109/CLOUD.2018.00114	en_US
dc.title	(WIP) Evaluation of a Cloud-Based System for Delivering Adaptive Micro Open Education Resource to Fresh Learners	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-July	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2018-July	en_US

Abstract:

© 2018 IEEE. In this paper, we present an online computation approach implemented in a cloud-based system to assist open education resource (OER) providers and instructors dealing with the sparsity of data in micro OER recommendation. An algorithmic framework is provided to realize the novel micro OER recommendation system based on heuristic rules. These rules can also optimize the approaches to blending new-coming micro OERs into established learning paths. Comparing with different widely used recommender systems, our evaluation shows the proposed heuristic algorithms for online computation performs satisfactorily in terms of precision and recall values.

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http://hdl.handle.net/10453/130256