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A study of feature selection algorithms for predicting students academic performance

The main aim of all the educational organizations is to improve the quality of education and elevate the academic performance of students. Educational Data Mining (EDM) is a growing research field which helps academic institutions to improve the performance of their students. The academic institutio...

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Main Authors: Zaffar, M., Savita, K.S., Hashmani, M.A., Rizvi, S.S.H.
Format: Article
Published: Science and Information Organization 2018
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049516304&doi=10.14569%2fIJACSA.2018.090569&partnerID=40&md5=7e4c2d2c412385558d50864f2ddd724a
http://eprints.utp.edu.my/21304/
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spelling my.utp.eprints.213042019-02-26T03:17:45Z A study of feature selection algorithms for predicting students academic performance Zaffar, M. Savita, K.S. Hashmani, M.A. Rizvi, S.S.H. The main aim of all the educational organizations is to improve the quality of education and elevate the academic performance of students. Educational Data Mining (EDM) is a growing research field which helps academic institutions to improve the performance of their students. The academic institutions are most often judged by the grades achieved by the students in examination. EDM offers different practices to predict the academic performance of students. In EDM, Feature Selection (FS) plays a vital role in improving the quality of prediction models for educational datasets. FS algorithms eliminate unrelated data from the educational repositories and hence increase the performance of classifier accuracy used in different EDM practices to support decision making for educational settings. The good quality of educational dataset can produce better results and hence the decisions based on such quality dataset can increase the quality of education by predicting the performance of students. In the light of this mentioned fact, it is necessary to choose a feature selection algorithm carefully. This paper presents an analysis of the performance of filter feature selection algorithms and classification algorithms on two different student datasets. The results obtained from different FS algorithms and classifiers on two student datasets with different number of features will also help researchers to find the best combinations of filter feature selection algorithms and classifiers. It is very necessary to put light on the relevancy of feature selection for student performance prediction, as the constructive educational strategies can be derived through the relevant set of features. The results of our study depict that there is a 10 difference of prediction accuracies between the results of datasets with different number of features. © 2015 The Science and Information (SAI) Organization Limited. Science and Information Organization 2018 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049516304&doi=10.14569%2fIJACSA.2018.090569&partnerID=40&md5=7e4c2d2c412385558d50864f2ddd724a Zaffar, M. and Savita, K.S. and Hashmani, M.A. and Rizvi, S.S.H. (2018) A study of feature selection algorithms for predicting students academic performance. International Journal of Advanced Computer Science and Applications, 9 (5). pp. 541-549. http://eprints.utp.edu.my/21304/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description The main aim of all the educational organizations is to improve the quality of education and elevate the academic performance of students. Educational Data Mining (EDM) is a growing research field which helps academic institutions to improve the performance of their students. The academic institutions are most often judged by the grades achieved by the students in examination. EDM offers different practices to predict the academic performance of students. In EDM, Feature Selection (FS) plays a vital role in improving the quality of prediction models for educational datasets. FS algorithms eliminate unrelated data from the educational repositories and hence increase the performance of classifier accuracy used in different EDM practices to support decision making for educational settings. The good quality of educational dataset can produce better results and hence the decisions based on such quality dataset can increase the quality of education by predicting the performance of students. In the light of this mentioned fact, it is necessary to choose a feature selection algorithm carefully. This paper presents an analysis of the performance of filter feature selection algorithms and classification algorithms on two different student datasets. The results obtained from different FS algorithms and classifiers on two student datasets with different number of features will also help researchers to find the best combinations of filter feature selection algorithms and classifiers. It is very necessary to put light on the relevancy of feature selection for student performance prediction, as the constructive educational strategies can be derived through the relevant set of features. The results of our study depict that there is a 10 difference of prediction accuracies between the results of datasets with different number of features. © 2015 The Science and Information (SAI) Organization Limited.
format Article
author Zaffar, M.
Savita, K.S.
Hashmani, M.A.
Rizvi, S.S.H.
spellingShingle Zaffar, M.
Savita, K.S.
Hashmani, M.A.
Rizvi, S.S.H.
A study of feature selection algorithms for predicting students academic performance
author_facet Zaffar, M.
Savita, K.S.
Hashmani, M.A.
Rizvi, S.S.H.
author_sort Zaffar, M.
title A study of feature selection algorithms for predicting students academic performance
title_short A study of feature selection algorithms for predicting students academic performance
title_full A study of feature selection algorithms for predicting students academic performance
title_fullStr A study of feature selection algorithms for predicting students academic performance
title_full_unstemmed A study of feature selection algorithms for predicting students academic performance
title_sort study of feature selection algorithms for predicting students academic performance
publisher Science and Information Organization
publishDate 2018
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049516304&doi=10.14569%2fIJACSA.2018.090569&partnerID=40&md5=7e4c2d2c412385558d50864f2ddd724a
http://eprints.utp.edu.my/21304/
_version_ 1738656270611644416
score 13.211869

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