A New Approach to Grouping Learners Based on Behavioral Engagement in CSCL Environments

Abstract

In Computer-Supported Collaborative Learning (CSCL) environments, forming a group is essential for the success of the learning process. Furthermore, several studies on forming groups in CSCL environments have been conducted recently to form ones that promote learners’ engagement and collaboration among their members. Forming group-based approaches requires data on learners’ actions (or traces) during the learning process. In this study, behavioral traces of learners are used to form groups. In other words, we used a clustering algorithm based on learners’ behavioral engagement to form homogeneous groups of learners. The learners must have different levels of engagement within each group to enhance their engagement and cognitive levels. The basis of the proposed grouping algorithm is a set of indicators of learners’ engagement. Furthermore, the proposed approach is based on an artificial intelligence algorithm, the k-means clustering method, which is used to find the maximum possibilities for the best clusters. Then, another algorithm is applied to obtain groups of learners with different levels of behavioral engagement. The validation of the proposed approach on a dataset containing behavioral traces from 100 learners was encouraging and promoting.

Keywords

• CSCL
• Group formation
• K-means
• Learning analytics
• Learner engagement

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