Group Formation based on Students’ Learning Styles by Circular Genetic Algorithm

Student group formation plays a critical role in promoting academic learning styles. Therefore, in this study, we employed a heuristic algorithm called the circular genetic algorithm (CGA) to form balanced groups of dissimilar students in terms of personal preferred learning styles. The benefit of balancing students’ learning styles is to assist students with diverse abilities to work collaboratively. With the assistance of our developed web application, members within a group contain mixed characteristics, while all formed groups of students are similar. In addition, an empirical case study was used to verify the feasibility and effectiveness of the proposed algorithm. Corresponding results verified by the analysis of variance (ANOVA) indicate that the algorithm can optimally allocate students in an efficient way