Performance Analysis of Parallel Particle Swarm Optimization Based Clustering of Students

Govindarajan, K.; Boulanger, David; Seanosky, Jeremie; Bell, Jason; Pinnell, Colin; Kumar, Vive; Kinshuk, Kinshuk; Somasundaram, Thamarai Selvi

doi:10.1109/icalt.2015.136

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…All other papers' recommendations will perform well on data sets that do not fit into the big data category. However, the same models used in these papers will face performance and accuracy challenges as very much larger set of data, the frequency of data arrival rate and processing complexity of continuous data is introduced in big data environments [63]. Fig.…”

Section: Big Data Consideration In Student Performance Prediction Modmentioning

confidence: 99%

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Systematic mapping review on student’s performance analysis using big data predictive model

Muthukrishnan

Govindasamy²,

Mustapha

2018

J. Fundam and Appl Sci.

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This paper classify the various existing predicting models that are used for monitoring and improving students' performance at schools and higher learning institutions. It analyses all the areas within the educational data mining methodology. Two databases were chosen for this study and a systematic mapping study was performed. Due to the very infant stage of this research area, only 114 articles published from 2012 till 2016 were identified. Within this, a total of 59 articles were reviewed and classified. There is an increased interest and research in the area of educational data mining, particularly in improving students' performance with various predictive and prescriptive models. Most of the models are devised for pedagogical improvements ultimately. It is a huge scarcity in producing portable predictive models that fits into any educational environment. There is more research needed in the educational big data.

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Section: Big Data Consideration In Student Performance Prediction Modmentioning

confidence: 99%

“…In [63], it address big data challenges in students' performance predictive model through Parallel Swarm Optimization (PPSO) based clustering mechanism. PPSO will reduce the processing time of clustering of students based on their ability, quality and efficiency.…”

Section: Big Data Consideration In Student Performance Prediction Modmentioning

confidence: 99%

Systematic mapping review on student’s performance analysis using big data predictive model

Muthukrishnan

Govindasamy²,

Mustapha

2018

J. Fundam and Appl Sci.

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Assessing Learners’ Progress in a Smart Learning Environment using Bio-Inspired Clustering Mechanism

Govindarajan

Boulanger

Seanosky

et al. 2016

Innovations in Smart Learning

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Optimal virtual machine selection for anomaly detection using a swarm intelligence approach

Selvaraj

Patan

Gandomi

et al. 2019

Applied Soft Computing

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Performance Analysis of Parallel Particle Swarm Optimization Based Clustering of Students

Cited by 4 publications

References 15 publications

Systematic mapping review on student’s performance analysis using big data predictive model

Systematic mapping review on student’s performance analysis using big data predictive model

Assessing Learners’ Progress in a Smart Learning Environment using Bio-Inspired Clustering Mechanism

Optimal virtual machine selection for anomaly detection using a swarm intelligence approach

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