2015 IEEE 15th International Conference on Advanced Learning Technologies (ICALT) (2015)
July 6, 2015 to July 9, 2015
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.
Clustering algorithms, Program processors, Particle swarm optimization, Acceleration, Computational modeling, Writing, Atmospheric measurements,hadoop distributed file system (HDFS), e-learning, learning analytics, clustering, parallel particle swarm optimization (PPSO), parallel processing
Kannan Govindarajan, David Boulanger, Jeremie Seanosky, Jason Bell, Colin Pinnell, Vivekanandan Suresh Kumar, Kinshuk, Thamarai Selvi Somasundaram, "Performance Analysis of Parallel Particle Swarm Optimization Based Clustering of Students", 2015 IEEE 15th International Conference on Advanced Learning Technologies (ICALT), vol. 00, no. , pp. 446-450, 2015, doi:10.1109/ICALT.2015.136