Manuscript received April 17, 2023; revised May 22, 2023; accepted June 21, 2023.
Abstract—In this research article, we propose a method based
on genetic algorithms to optimize the grouping of students in
engineering education. Our method aims to create student
groups that take into account their skills, preferences, and
relevant factors. We build upon previous research that has
successfully utilized genetic algorithms for group formation in
various contexts, such as assigning students to laboratory
groups and facilitating cooperative learning. We implement and
evaluate our proposed methods in collaborative learning
environment, examining their impact on collaborative
performance, processes, and perceptions. The results of our
research demonstrate that grouping methods supported by
genetic algorithms positively influence performance and
collaborative processes, while students perceive these methods
as fair and effective. This article makes a valuable contribution
to the field of engineering education by providing methods that
up to minus student grouping, considering their initial
characteristic and performance and preferences. By employing
these methods, the quality of group work can be enhanced
leading to improve student learning experiences. Future
research can explore the application of the of this method in
order educational settings and investigate the factors that
influence their effectiveness.
Index Terms—Class grouping, optimization, genetic
algorithm
The authors are affiliated with the Department of Electronics Engineering,
Faculty of Engineering, Universitas Negeri Padang, Padang, Indonesia.
*Correspondence: dennykurniadi@ft.unp.ac.id (D.K.)
Cite: Denny Kurniadi*, Hendra Hidayat, Muhammad Anwar, Khairi Budayawan, Abdurrasyid Luthfi Syaifar, Zulhendra, Efrizon, and Rahmadona Safitri, "Genetic Algorithms for Optimizing Grouping of Students Classmates in Engineering Education," International Journal of Information and Education Technology vol. 13, no. 12, pp. 1907-1916, 2023.
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).