Paper Info
Title
Particle Swarm Optimization (PSO)-Based Clustering for Improving the Quality of Learning using Cloud Computing
Abstract
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).
Year
DOI
Venue
2013
10.1109/ICALT.2013.160
Advanced Learning Technologies
Keywords
Field
DocType
clustering process,processed data,particle swarm optimization,intra cluster distance,effective teaching,virtual learning,simulation process,cloud computing,k-means algorithm,file system,inter cluster,servers,distributed processing,programming,data clustering,distributed databases,clustering algorithms,teaching,clustering,data generation,data analysis,algorithm design and analysis
Particle swarm optimization,Population,Virtual learning environment,Data mining,Learning Management,Computer science,Artificial intelligence,Cluster analysis,Test data generation,Machine learning,Metaheuristic,Cloud computing
Conference
ISSN
Citations 
PageRank 
2161-3761
5
0.45
References 
Authors
0
4
Name
Order
Citations
PageRank
Kannan Govindarajan115013.37
Thamarai Selvi Somasundaram29610.15
Vivekanandan Suresh Kumar3167.04
Kinshuk42123389.31