doi: 10.18178/ijiet.2023.13.12.2004
Genetic Algorithms for Optimizing Grouping of Students Classmates in Engineering Education
Denny Kurniadi*
, , , , , , ,
- Manuscript receivedApril 17, 2023
- revisedMay 22, 2023
- acceptedJune 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.
Keywords
- Class grouping
- optimization
- genetic algorithm
How to 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. https://doi.org/10.18178/ijiet.2023.13.12.2004
Copyright & License
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).