Personalized e-learning using shuffled frog-leaping algorithm

One of the main problems related with the design of e-learning is the current composition approaches do not support “personalized-learning”, that is, not take into account the difference in the prior knowledge of the learner and his learning ability. In order to provide solution for this problem, various e-course composition approaches have been proposed to use various techniques like Genetic Algorithm and Particle swarm optimization. This paper proposes an improved personalized e-course composition approach using shuffled frog-leaping algorithm (SFLA). The results of the simulations performed demonstrate that the proposed approach is a good solution to the problem raised. In addition, the method is compared with genetic algorithms and particle swarm optimization.