Swarm intelligence grouping of e-learners using fuzzy inspired PSO method

Recent advances in technology and the integration of these advances in instructional design have led to a mass individualization where personalized instruction is offered simultaneously to large groups of learners. The first step to adapt instruction is forming different groups of learners based on their attributes. Many methods have used to form learners’ groups in e-learning environment specially data mining techniques such as clustering methods. This paper aims to propose a clustering method to group learners based on their cognitive style and using some specific learners’ observable behaviors while working by system. The objective function of proposed method is defined by considering two criteria in measuring the clustering goodness, compactness and separation, and Particle Swarm Optimization (PSO) method is used to optimize the objective function. This method used to group learners based on cognitive style. Results of the proposed method are compared with K-means, fuzzy C-means, and EFC methods using Davies-Bouldin clustering validity index and comparing the achieved groups based on the cognitive style of learners who are in the same group, shows that the grouping accuracy is in a higher level using fuzzy-inspired PSO method and this method has the better clustering performance than the others and groups similar learners in one cluster.