iGLS: Intelligent Grouping for Online Collaborative Learning

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.

• Type: Conference
• Year: 2009
• Where: ICALT
• Authors: Shuangyan Liu, Mike Joy, Nathan Griffiths
• Keywords: Collaborative Learning, Collaborative Learning Environments, ICALT 2009, Intelligent Grouping, Machine Learning