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Dimensionality reduction technique for developing undergraduate student dropout model using principal component analysis through R package

Publication Type : Conference Paper

Publisher : 2016 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2016

Source : 2016 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2016, Institute of Electrical and Electronics Engineers Inc. (2017)

Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020030818&doi=10.1109%2fICCIC.2016.7919670&partnerID=40&md5=1b7fbe60ff4b6884fabf1e9ba947499c

ISBN : 9781509006113

Keywords : Artificial intelligence, Behavioral, Classification (of information), Correlation methods, Covariance matrix, dropout, Education, eigenvalues and eigenfunctions, factoextra, FactoMineR, Principal component analysis, Student surveys, Students, Undergraduate

Campus : Mysuru

School : School of Arts and Sciences

Department : Computer Science

Verified : Yes

Year : 2017

Abstract : Every educational institute feels proud when its admission closes with expected number of students. The prospective student enters the campus with lots of hopes, dreams and expectations. When their expectations are not met or if they undergo for critical circumstances and makes them drop from their registered program. Predicting undergraduate student dropouts are a major challenge in educational system due to the multidimensionality of data. This paper focuses on dimensionality reduction of multi-behavioral attributes of a 150 students with 51 attribute to identify the factor that affects the early dropout. The dataset dimensionality is reduced through Principal Component Analysis by obtaining the Eigenvalues and Eigenvectors from the covariance matrix by transforming the original attribute into new set attribute without losing the information. Visualization is done with a help of R package factoextra and FactoMineR. The further dataset can be used for classification. The discovery of concealed knowledge can be used for better academic planning and early prediction of student dropout. © 2016 IEEE.

Cite this Research Publication : Vinayak M. Hegde, “Dimensionality reduction technique for developing undergraduate student dropout model using principal component analysis through R package”, in 2016 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2016, 2017.

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