Performance Analysis of Parallel Particle Swarm Optimization Based Clustering of Students

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 assessme...

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Veröffentlicht in:Proceedings (IEEE International Conference on Advanced Learning Technologies) S. 446 - 450
Hauptverfasser: Govindarajan, Kannan, Boulanger, David, Seanosky, Jeremie, Bell, Jason, Pinnell, Colin, Kumar, Vivekanandan Suresh, Kinshuk, Somasundaram, Thamarai Selvi
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2015
Schlagworte:
Acceleration
Atmospheric measurements
clustering
Clustering algorithms
Computational modeling
e-learning
hadoop distributed file system (HDFS)
learning analytics
parallel particle swarm optimization (PPSO)
parallel processing
Particle swarm optimization
Program processors
Writing
ISSN:2161-3761
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Zusammenfassung: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.
ISSN:2161-3761
DOI:10.1109/ICALT.2015.136