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The development of a predictive model for students’ final grades using machine learning techniques

As per research, utilizing predictive analytics in education can be very beneficial. It can help educators improve students' performance by analyzing historical data through various approaches such as data mining and machine learning. However, there is a scarcity of studies on using machine lea...

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Main Authors: N. H., A. Rahman, Sahimel Azwal, Sulaiman, Nor Azuana, Ramli
Format: Article
Language:English
Published: Penerbit UMP 2023
Subjects:
QA Mathematics
Online Access:http://umpir.ump.edu.my/id/eprint/40003/1/The%20development%20of%20apredictive%20model%20for%20students.pdf
http://umpir.ump.edu.my/id/eprint/40003/
https://doi.org/10.15282/daam.v4i1.9591
https://doi.org/10.15282/daam.v4i1.9591
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spelling my.ump.umpir.400032024-01-15T04:04:37Z http://umpir.ump.edu.my/id/eprint/40003/ The development of a predictive model for students’ final grades using machine learning techniques N. H., A. Rahman Sahimel Azwal, Sulaiman Nor Azuana, Ramli QA Mathematics As per research, utilizing predictive analytics in education can be very beneficial. It can help educators improve students' performance by analyzing historical data through various approaches such as data mining and machine learning. However, there is a scarcity of studies on using machine learning and predictive analytics to enhance student performance in Malaysian higher education. This study used the records of 450 students enrolled in the Business Statistics course at Universiti Islam Pahang Sultan Ahmad Shah (UnIPSAS) from 2013, obtained from UnIPSAS's Learning Management System. The aim was to develop the best predictive model for forecasting students' final grades based on their performance levels, using machine learning techniques such as Decision Tree, k-Nearest Neighbor, and Naïve Bayes. The final model was developed using Python software. The results showed a strong negative correlation between the students' carry marks and their final grades, with an r-value of -0.8. Naïve Bayes was found to be the best model, with an AUC score of 0.79. Penerbit UMP 2023-04 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/40003/1/The%20development%20of%20apredictive%20model%20for%20students.pdf N. H., A. Rahman and Sahimel Azwal, Sulaiman and Nor Azuana, Ramli (2023) The development of a predictive model for students’ final grades using machine learning techniques. Data Analytics and Applied Mathematics (DAAM), 4 (1). pp. 40-48. ISSN 2773-4854. (Published) https://doi.org/10.15282/daam.v4i1.9591 https://doi.org/10.15282/daam.v4i1.9591
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
N. H., A. Rahman
Sahimel Azwal, Sulaiman
Nor Azuana, Ramli
The development of a predictive model for students’ final grades using machine learning techniques
description As per research, utilizing predictive analytics in education can be very beneficial. It can help educators improve students' performance by analyzing historical data through various approaches such as data mining and machine learning. However, there is a scarcity of studies on using machine learning and predictive analytics to enhance student performance in Malaysian higher education. This study used the records of 450 students enrolled in the Business Statistics course at Universiti Islam Pahang Sultan Ahmad Shah (UnIPSAS) from 2013, obtained from UnIPSAS's Learning Management System. The aim was to develop the best predictive model for forecasting students' final grades based on their performance levels, using machine learning techniques such as Decision Tree, k-Nearest Neighbor, and Naïve Bayes. The final model was developed using Python software. The results showed a strong negative correlation between the students' carry marks and their final grades, with an r-value of -0.8. Naïve Bayes was found to be the best model, with an AUC score of 0.79.
format Article
author N. H., A. Rahman
Sahimel Azwal, Sulaiman
Nor Azuana, Ramli
author_facet N. H., A. Rahman
Sahimel Azwal, Sulaiman
Nor Azuana, Ramli
author_sort N. H., A. Rahman
title The development of a predictive model for students’ final grades using machine learning techniques
title_short The development of a predictive model for students’ final grades using machine learning techniques
title_full The development of a predictive model for students’ final grades using machine learning techniques
title_fullStr The development of a predictive model for students’ final grades using machine learning techniques
title_full_unstemmed The development of a predictive model for students’ final grades using machine learning techniques
title_sort development of a predictive model for students’ final grades using machine learning techniques
publisher Penerbit UMP
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40003/1/The%20development%20of%20apredictive%20model%20for%20students.pdf
http://umpir.ump.edu.my/id/eprint/40003/
https://doi.org/10.15282/daam.v4i1.9591
https://doi.org/10.15282/daam.v4i1.9591
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score 13.235362

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