الفهرس 
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Abstract
The main objective of e-learning systems is to improve the student- learning performance and satisfaction. This can be achieved by providing a personalized learning experience that identifies and satisfies the individual learns’ requirements and abilities. The performance of the e-learning systems can be significantly improved by exploiting dynamic self-learning capabilities that rapidly adapts to prior user interactions within the system and the continuous changes in the environment.
In this thesis, a dynamic multi-agent system using particle swarm optimization (DMAPSO) for e-learning systems is proposed. The system incorporates five agents that take into consideration the variations in the capabilities among the different users. First, the Project Clustering Agent (PCA) is used to cluster a set of learning resources/projects into similar groups. Second, the Student Clustering Agent (SCA) groups students according to their preferences and abilities. Third, the Student-Project Matching Agent (SPMA) is used to map each learner’s group to a suitable project or particular learning resources according to specific design criteria. Fourth, the Student-Student Matching Agent (SSMA) is designed to perform the efficient mapping between different students. Finally, the Dynamic Student Clustering Agent (DSCA) is employed to continually tracks and analyzes the student’s behavior within the system such as changes in knowledge and skill levels. Consequently, the DSCA adapts the e-learning environments to accommodate these variations. Several groups of
experiments were performed to evaluate the performance of the proposed DMAPSO algorithm.
The objective of the first group of experiments is to investigate the efficiency of the proposed PCA and SCA algorithms. Experimental results show that subtractive-PSO algorithm presents efficient clustering results in comparison with conventional PSO and subtractive clustering algorithms .
In the second and third groups, the performances of the SPMA and SSMA are compared with competing approaches. It shows that the average fitness values obtained by SPMA and SSMA were similar to the optimal solutions obtained by the Exhaustive search and significantly better than the values obtained by RSFS, and the average execution time of the proposed system was similar to that of RSFS and is significantly less than the time required by the Exhaustive search, particularly for large data banks.
Finally, in the fourth group, the performance of the DSCA is evaluated. The experiments examine the effect of the key design parameters and compare the performance of DSCA to other algorithms in the dynamic environment; no change is performed, re-randomize 15% of particles, re-randomize all particles and DSCA. The DSCA clustering approach generates the clustering result that has the minimal fitness values across all the datasets