Abstract
Cooperative learning is an instructional approach in which students work together in small groups in order to achieve a common academic goal. In the context of cooperative learning, students in classrooms tend to learn more by sharing their experiences and knowledge. In addition, a diversity of educational backgrounds and student learning styles can be used to build heterogeneous groups of students. In this paper, we propose an approach for the group composition, regarding the Index of Learning Styles (ILS) questionnaire and prior educational knowledge in order to achieve the mechanism for equity among groups and ensure that heterogeneous students are distributed optimally within the group formation. This causes the search for an optimized group composition of all students more complex and becomes a time-consuming task. Therefore, the proposed algorithm mimics the natural process of a genetic algorithm in order to achieve optimal solutions. In addition, we have implemented our algorithm to construct student groups. Our experiment shows that the algorithm enhances the quality of the group formation of heterogeneous students leading to better solutions.
Highlights
The cooperative learning among students in groups is supported by several researchers because it is an educational technique used to manage learning activities in classrooms
An approach called Genetic Algorithm for Forming Student Groups (GAFSG) was presented in order to generate student groups in a heterogeneous way by using genetic algorithms (GAs)
The proposed approach adapts some dimensions of the Felder-Silverman model to grouping students
Summary
The cooperative learning among students in groups is supported by several researchers because it is an educational technique used to manage learning activities in classrooms. Some studies have focused on improving cooperative learning for students, including the work by Balmeceda, Schiaffino, and Pace [3] In this article, they claimed that different characteristics of group members might influence the group performance. Some researchers documented that learning style-based group formation helps promoting heterogeneity groups and has a beneficial impact on learning in cooperative learning environments, as seen in [14,15]. These reasons have given way to the proposal of a genetic-algorithm approach to form student groups by using both student learning styles and student educational background as the main criteria to allocate students in group formation.
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