An Evolutionary Algorithm for Homogeneous Grouping to Enhance Web-based Collaborative Learning

Grouping of students is an important educational activity in traditional learning and e-learning environments and lots of research has been done in this area. In this paper the new algorithm is proposed for grouping of students with unlimited features. Our proposed algorithm considers the priority of features as well as their values. Priority of features is involved in the grouping by taking advantage of the Inversion concept. The results indicate that our algorithm is successful in both intra-fitness and inter-fitness grouping criteria. The discrepancy between the members of group that is called intra-group fitness and the similarity between heterogeneous formed groups that is called inter-fitness group.