Ranking of influencing factors in predicting students' academic performance.

The aim of this study was to rank influencing factors that contribute to the prediction of students’ academic performance. It is useful in identifying weak students who are likely to perform poorly in their studies. In this study, we used WEKA open source data mining tool to analyze attributes for p...

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Main Authors: Affendey, Lilly Suriani, Mohd Paris, Ikmal Hisyam, Mustapha, Norwati, Sulaiman, Md. Nasir, Muda, Zaiton
Format: Article
Language:English
English
Published: Asian Network for Scientific Information (ANSINET) 2010
Online Access:http://psasir.upm.edu.my/id/eprint/16423/1/Ranking%20of%20influencing%20factors%20in%20predicting%20students.pdf
http://psasir.upm.edu.my/id/eprint/16423/
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spelling my.upm.eprints.164232015-11-03T06:45:53Z http://psasir.upm.edu.my/id/eprint/16423/ Ranking of influencing factors in predicting students' academic performance. Affendey, Lilly Suriani Mohd Paris, Ikmal Hisyam Mustapha, Norwati Sulaiman, Md. Nasir Muda, Zaiton The aim of this study was to rank influencing factors that contribute to the prediction of students’ academic performance. It is useful in identifying weak students who are likely to perform poorly in their studies. In this study, we used WEKA open source data mining tool to analyze attributes for predicting a higher learning institution’s bachelor of computer science students’ academic performance. The data set comprised of 2427 number of student records and 396 attributes of students registered between year 2000 and 2006. Preprocessing includes attribute importance analysis. We applied the data set to different classifiers (Bayes, trees or function) and obtained the accuracy of predicting the students’ performance into either first-second-upper class or second-lower-third class. A cross-validation with 10 folds was used to evaluate the prediction accuracy. Our results showed the ranking of courses that has significant impact on predicting the students’ overall academic results. In addition, we perform experiments comparing the performance of different classifiers and the result showed that Naïve Bayes, AODE and RBFNetwork classifiers scored the highest percentage of prediction accuracy of 95.29%. Asian Network for Scientific Information (ANSINET) 2010 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/16423/1/Ranking%20of%20influencing%20factors%20in%20predicting%20students.pdf Affendey, Lilly Suriani and Mohd Paris, Ikmal Hisyam and Mustapha, Norwati and Sulaiman, Md. Nasir and Muda, Zaiton (2010) Ranking of influencing factors in predicting students' academic performance. Information Technology Journal, 9 (4). pp. 832-837. ISSN 1812-5638 English
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
English
description The aim of this study was to rank influencing factors that contribute to the prediction of students’ academic performance. It is useful in identifying weak students who are likely to perform poorly in their studies. In this study, we used WEKA open source data mining tool to analyze attributes for predicting a higher learning institution’s bachelor of computer science students’ academic performance. The data set comprised of 2427 number of student records and 396 attributes of students registered between year 2000 and 2006. Preprocessing includes attribute importance analysis. We applied the data set to different classifiers (Bayes, trees or function) and obtained the accuracy of predicting the students’ performance into either first-second-upper class or second-lower-third class. A cross-validation with 10 folds was used to evaluate the prediction accuracy. Our results showed the ranking of courses that has significant impact on predicting the students’ overall academic results. In addition, we perform experiments comparing the performance of different classifiers and the result showed that Naïve Bayes, AODE and RBFNetwork classifiers scored the highest percentage of prediction accuracy of 95.29%.
format Article
author Affendey, Lilly Suriani
Mohd Paris, Ikmal Hisyam
Mustapha, Norwati
Sulaiman, Md. Nasir
Muda, Zaiton
spellingShingle Affendey, Lilly Suriani
Mohd Paris, Ikmal Hisyam
Mustapha, Norwati
Sulaiman, Md. Nasir
Muda, Zaiton
Ranking of influencing factors in predicting students' academic performance.
author_facet Affendey, Lilly Suriani
Mohd Paris, Ikmal Hisyam
Mustapha, Norwati
Sulaiman, Md. Nasir
Muda, Zaiton
author_sort Affendey, Lilly Suriani
title Ranking of influencing factors in predicting students' academic performance.
title_short Ranking of influencing factors in predicting students' academic performance.
title_full Ranking of influencing factors in predicting students' academic performance.
title_fullStr Ranking of influencing factors in predicting students' academic performance.
title_full_unstemmed Ranking of influencing factors in predicting students' academic performance.
title_sort ranking of influencing factors in predicting students' academic performance.
publisher Asian Network for Scientific Information (ANSINET)
publishDate 2010
url http://psasir.upm.edu.my/id/eprint/16423/1/Ranking%20of%20influencing%20factors%20in%20predicting%20students.pdf
http://psasir.upm.edu.my/id/eprint/16423/
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score 13.211869