Multi-agent System for Expert Evaluation of Learning Objects from Repository

Tabares, Valentina; Duque, Néstor D.; Ovalle, Demetrio Arturo

doi:10.1007/978-3-319-19033-4_27

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…In [35], authors investigated a hybrid fuzzy clustering method based on Fuzzy C-means and fuzzy PSO (FPSO) to gain the benefits of both algorithms. A multi-agent system (MAS) is a lightly joined network of problem-solvers that work collaboratively to solve complex problems that are beyond the capabilities of the individual solvers [36,37,38,39,40,41]. Several researchers proposed the use of multiple agents' implementation approach to deal with the complicated tasks.…”

Section: Introductionmentioning

confidence: 99%

“…Several researchers proposed the use of multiple agents' implementation approach to deal with the complicated tasks. This involves dividing the task of into several subtasks and handles these subtasks by employing several software agents [40,41]. Various attempts to develop MAS for Educational systems were presented in the literature [38,39,41].…”

Section: Introductionmentioning

confidence: 99%

“…This involves dividing the task of into several subtasks and handles these subtasks by employing several software agents [40,41]. Various attempts to develop MAS for Educational systems were presented in the literature [38,39,41]. In [39], authors proposed that a student in a learning environment should be placed within the framework of the surrounding entities that support the student's access to the learning resources and participation in different learning activities.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing Intelligent Agent Performance in E-Learning Environment

Al-Tarabily¹,

Marie²,

Al-Kader³

et al. 2018

Port-Said Engineering Research Journal

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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.

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