Paper Info
Title
Performance Analysis of Parallel Particle Swarm Optimization Based Clustering of Students
Abstract
While accurate computational models that embody learning efficiency remain a distant and elusive goal, big data learning analytics approaches this goal by recognizing competency growth of learners, at various levels of granularity, using a combination of continuous, formative, and summative assessments. Our earlier research employed the conventional Particle Swarm Optimization (PSO) based clustering mechanism to cluster large numbers of learners based on their observed study habits and the consequent growth of subject knowledge competencies. This paper describes a Parallel Particle Swarm Optimization (PPSO) based clustering mechanism to cluster learners. Using a simulation study, performance measures of quality of clusters such as the Inter Cluster Distance, the Intra Cluster Distance, the processing time and the acceleration values are estimated and compared.
Year
DOI
Venue
2015
10.1109/ICALT.2015.136
International Conference on Advanced Learning Technologies
Keywords
Field
DocType
e-learning, learning analytics, clustering, parallel particle swarm optimization (PPSO), parallel processing, hadoop distributed file system (HDFS)
Particle swarm optimization,Cluster (physics),Learning analytics,Summative assessment,Computer science,Multi-swarm optimization,Computational model,Artificial intelligence,Cluster analysis,Big data,Machine learning
Conference
ISSN
Citations 
PageRank 
2161-3761
1
0.35
References 
Authors
12
8
Name
Order
Citations
PageRank
Kannan Govindarajan115013.37
David Boulanger2115.38
Jeremie Seanosky382.99
Jason Bell410.35
Colin Pinnell510.35
Vivekanandan Suresh Kumar6167.04
Kinshuk72123389.31
Thamarai Selvi Somasundaram89610.15