Particle Swarm Optimization (PSO)-based Clustering for Improving the Quality of Learning using Cloud Computing

Virtual Learning is a key enabler for giving equal opportunity to all throughout the globe. However, the pedagogical approach preferred by a group of learners may differ from another set of learners. By providing different pedagogical approaches through virtual learning, it is possible to satisfy the need of the learners, thereby improving the quality of learning. To identify the preference or choice of the pedagogy, the behavior of the learners is captured and analyzed. According to the understanding capability, the appropriate pedagogy is adopted for that learner. The conventional Learning Management System (LMS) plays a major role for achieving effective teaching and learning process. However, the conventional LMS fails to address the effective teaching and learning process by not providing the contents based on individual user's ability. The proposed work mainly intends to capture the data from students, analyze and cluster the data based on their individual performances in terms of accuracy, efficiency and quality. The clustering process is carried out by employing the population-based metaheuristic algorithm of Particle Swarm Optimization (PSO). The simulation process is carried out by generating the data. The generated data is based on the real data collected from engineering undergraduate students. The proposed PSO-based clustering is compared with existing K-means algorithm for analyze the performance of inter cluster and intra cluster distances. Finally, the processed data is effectively stored in the Cloud resources using Hadoop Distributed File System (HDFS).