A Discrete Particle Swarm Optimization Approach to Compose Heterogeneous Learning Groups

Collaborative learning is an educational strategy which is popularly used in project-based courses in schools and colleges. The diversity of group members is frequently considered to be a crucial criterion that can promote intensive intra-group interaction and successful learning outcomes. Yet, when the number of students is up to several hundreds, it is challenging for instructors to look for an optimal group formation considering maximal diversity of students in every group. To address this problem, this paper presents a discrete particle swarm optimization approach to compose heterogeneous learning groups. We carried out simulations based on optimizing the heterogeneity of gender and personality type. The experimental results show that the proposed approach is an effective and stable method that can support instructors to compose heterogeneous collaborative learning groups.