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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| Vydáno v: | Proceedings (IEEE International Conference on Advanced Learning Technologies) s. 446 - 450 |
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IEEE
01.07.2015
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| ISSN: | 2161-3761 |
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| 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. |
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| AbstractList | 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. |
| Author | Kumar, Vivekanandan Suresh Somasundaram, Thamarai Selvi Seanosky, Jeremie Pinnell, Colin Govindarajan, Kannan Boulanger, David Bell, Jason Kinshuk |
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| Snippet | While accurate computational models that embody learning efficiency remain a distant and elusive goal, big data learning analytics approaches this goal by... |
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| SubjectTerms | 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 |
| Title | Performance Analysis of Parallel Particle Swarm Optimization Based Clustering of Students |
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