Designing and Developing of Learning Class Grouping Applications Base on Genetic Algorithms

Abstract

The student learning class grouping application is a crucial service within the academic lecture system to facilitate learning and make it easier for educators to choose strategies and teaching methods to optimize academic achievement. In this grouping application, a genetic algorithm is implemented to optimize the distribution of learning classes, adopting the concept of biological evolution where the initial population of learning groups is considered as "individuals" with information about different grouping criteria. Through the process of selection, crossover, and mutation, these individuals undergo evolution from generation to generation, where those with the highest fitness value (according to the specified criteria) are passed on to the next generation, while those with lower fitness values may be eliminated. This evolutionary process continues until an optimal learning group is obtained, with a combination of suitable and best criteria to achieve the desired intra-heterogeneous and interhomogeneous characteristics in learning.