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iGLS: Intelligent grouping for online collaborative learning

Liu, Shuangyan; Joy, Mike and Griffiths, Nathan (2009). iGLS: Intelligent grouping for online collaborative learning. In: Proceedings, The Ninth IEEE International Conference on Advanced Learning Technologies, IEEE, pp. 364–368.

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One of the factors that affect successful collaborative learning is the composition of collaborative groups. Due to the lack of intelligent grouping according to learners’ pedagogic needs in current online collaborative learning environments, developing intelligent grouping according to individual learners’ cognitive characteristics is highly desired. In this paper, we propose a new approach to supporting intelligent grouping based on learners’ learning styles. Our approach achieves the balance of different levels of learning styles in group composition. We demonstrate how it can fit into current activity-based collaborative learning environments and how it could be applied in a
real world application.

Item Type: Conference or Workshop Item
Copyright Holders: 2009 IEEE
ISBN: 0-7695-3711-1, 978-0-7695-3711-5
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 42139
Depositing User: Shuangyan Liu
Date Deposited: 10 Mar 2015 16:41
Last Modified: 09 Dec 2018 05:46
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