Improved Personalized E-Course Composition Approach using Modified Particle Swarm Optimization with Inertia-Coefficient

One of the main problems associated with the authoring of e-courses, for e-learning systems, is that the current composition-approaches do not support 'personalized-learning' or in other words, the current composition approaches fail to take into consideration the difference in individual learning-capabilities and the background knowledge of the individual learners and thus 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 proposed to use various methods of computational-optimization techniques like Genetic Algorithm and Particle swarm optimization. The primary purpose of this paper is to propose an improved personalized e-course composition approach using modified particle swarm optimization algorithm (MPSO) with inertia-coefficient, which intends to serve as an effective solution to the afore-mentioned problem. Various simulation-based experiments