Personalized e-course composition approach using digital pheromones in improved particle swarm optimization

One of the uphill tasks associated with the authoring of e-courses, for e-learning systems, is that the current composition techniques do not support `personalized-learning´ or in other words, the current composition methods fail to take into consideration the difference in individual learning capabilities and the background knowledge of the individual learners, which do not provide materials that exactly meet the demands of the individual learners. In order to provide solution for this problem, in the past, various e-course composition approaches had been proposed to use various methods of computational optimization techniques like genetic algorithm and particle swarm optimization. This paper proposes an improved personalized e-course composition approach based on modified particle swarm optimization algorithm along with digital pheromones. The final results of our ongoing research in this area, is furnished in this paper. Results of the various simulation-based experiments that have been conducted are furnished at the end of this paper. These results demonstrate that our proposed approach is an effective solution to the problem of `personalized learning´. In addition, our proposed approach is compared with the existing approaches, which uses Basic particle swarm optimization algorithm (BPSO) and modified PSO algorithm. These comparisons demonstrate that our proposed model is more efficient than others.