Genetic algorithms for online remedial education based on competency approach

Personalized learning is one of the main problems associated with the online remedial education (ORE) for e_learning systems. The current composition approaches fail to take into consideration the difference in individual learning-competency and the background knowledge of the individual learners and thus don't provide the adequate teaching sequence that exactly meets the demands of the individual learners. In order to provide solution for this problem, we propose to use Genetic Algorithms (GAs) to configure personalized ORE for individual learners and maximize their success degree. To validate the practicability of the proposed approach, we investigate the achievements of students in actual learning environments. The investigated results show that the learning achievements of the students who are provided personalized ORE with our approach are better than the students who are provided a conventional uniform remedial education.