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Reseach Article
Improved personalized e-course composition approach using modified particle swarm optimization with inertia-coefficient
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 6 |
| Year of Publication: 2010 |
| Authors: Dheeban S.G, Deepak V, Dhamodharan L, Susan Elias |
10.5120/134-252
|
Dheeban S.G, Deepak V, Dhamodharan L, Susan Elias . Improved personalized e-course composition approach using modified particle swarm optimization with inertia-coefficient. International Journal of Computer Applications. 1, 6 ( February 2010), 102-107. DOI=10.5120/134-252
Abstract
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 were conducted and the results of these experiments have been 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, these graphs compare our proposed approach with an existing approach which uses Basic particle swarm optimization algorithm (BPSO). These comparisons demonstrate the efficiency of our proposed model.
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