Abstract

One goal of collaborative learning is to maximize the learning performance of all participating students. In order to achieve this aim, the first step is to consider how to assist instructors in forming well-structured collaborative learning groups with a good work atmosphere to promote successful outcomes for all members. Generally, understanding levels and interests of students are two grouping criteria that are usually considered by practicing instructors. Nevertheless, when the instructors face a large number of students, simultaneously considering the two grouping criteria to form the students in an appropriate collaborative learning context is almost impossible. To address this problem, this study formulates a group composition problem to model the formation of collaborative learning groups that satisfy the two grouping criteria. Moreover, this study is based on a novel approach called particle swarm optimization (PSO) to propose an enhanced PSO (EPSO) for composing well-structured collaborative learning groups. In addition, the experimental results have demonstrated that the proposed approach is an applicable and robust method that can aid instructors in planning different kinds of collaborative learning processes.

Citation

Lin, Y.T., Huang, Y.M. & Cheng, S.C. An automatic group composition system for composing collaborative learning groups using enhanced particle swarm optimization. Computers & Education, 55(4), 1483-1493. Elsevier Ltd. Retrieved August 16, 2024 from https://www.learntechlib.org/p/201395/.

Keywords

• Cooperative/collaborative learning
• evaluation methodologies
• Public spaces and computing