Optimizing Intelligent Agent Performance in E-Learning Environment

Al-Tarabily, Mariam; Marie, Mahmmoud; Abd Al-Kader, Rehab; Abd Al-Azem, Gammal

doi:10.21608/pserj.2018.32473

Optimizing Intelligent Agent Performance in E-Learning Environment

Document Type : Original Article

Authors

¹ Electrical Engineering Department, Faculty of Engineering, Port-Said University, Port-Fouad, EGYPT

² Computers and Systems Engineering, Department, Faculty of Engineering, Al-Azhar University, Cairo, EGYPT

10.21608/pserj.2018.32473

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 learner's requirements and abilities. The performance of the e-learning systems can be significantly improved by exploiting dynamic selflearning capabilities that rapidly adapts to prior user interactions within the system and the continuous changes in the environment. In this paper, 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. Experimental results demonstrate the effectiveness of the proposed system in providing near-optimal solutions in considerably less computational time.

Keywords