Interaction driven composition of student groups for optimal groupwork learning performance

Collaborative Learning (CL) has been considered as an effective way to improve the learning outcomes of students in contrast to individual learning. However, assigning a groupwork task to a team of students does not guarantee a successful performance, and in fact could hinder the benefits of group learning if the members do not interact as expected. Indeed, group learning performance is largely dependent on group composition. In this work we address the challenge of identifying the characteristics of the individual group members that bare the significant impact on the performance of the groupwork. Specifically we investigate the impact that a combination of individual student performances and gender have on the group performance and intend to find generic segmentation guidelines that would map smoothly onto the groupwork performance. A novel grouping method is proposed, which splits the set of students into groups that maximize one of the two desired criteria: the expected average groupwork performance or the average improvement achieved by a student as a result of synergic group learning and interaction effects. The model uses global optimization approach to identify optimal students allocation into the groups that best satisfy the optimization criteria. We illustrate our findings on the data obtained from the trial of the Collaborative Learning Environment (CLE) software. The CLE was developed at Etisalat British Telecom Innovation Centre (EBTIC) and tested over one semester with a sample of 122 students working in different groups in the Engineering and Molecular Biology courses at Khalifa University. The results of our method can not only help to understand the significant factors impacting group performance in group-based learning, but can also provide practical strategies on optimal group composition for collaborative learning activities.