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An investigation into student performance prediction using regularized logistic regression

The problem of university dropout poses a significant challenge to education systems worldwide, affecting administrators, teachers, and students. Early identification and intervention strategies are crucial for addressing this issue. In addition, advances in machine learning have paved the way for m...

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Main Authors: Kurniadi, Felix Indra, Dewi, Meta Amalya, Murad, Dina Fitria, Rabiha, Sucianna Ghadati, Awanis, Romli
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/41898/1/An%20investigation%20into%20student%20performance%20prediction.pdf
http://umpir.ump.edu.my/id/eprint/41898/2/An%20investigation%20into%20student%20performance%20prediction%20using%20regularized%20logistic%20regression_ABS.pdf
http://umpir.ump.edu.my/id/eprint/41898/
https://doi.org/10.1109/ICCED60214.2023.10425782
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spelling my.ump.umpir.418982024-08-30T00:14:54Z http://umpir.ump.edu.my/id/eprint/41898/ An investigation into student performance prediction using regularized logistic regression Kurniadi, Felix Indra Dewi, Meta Amalya Murad, Dina Fitria Rabiha, Sucianna Ghadati Awanis, Romli QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) The problem of university dropout poses a significant challenge to education systems worldwide, affecting administrators, teachers, and students. Early identification and intervention strategies are crucial for addressing this issue. In addition, advances in machine learning have paved the way for more accurate predictions of student performance. This paper investigates the use of regularization techniques, specifically Lasso (L1) and Ridge (L2) regularization, within logistic regression models to improve the accuracy of performance prediction. This research's dataset was obtained from the Binus Online Learning platform at Bina Nusantara University, with a focus on the Information System study program between 2020 and 2021. The results reveal that logistic regression with regularization achieves a high level of accuracy, recall, and precision in predicting student performance. The findings contribute to the development of an early warning system to identify at-risk students, enabling timely intervention and support. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41898/1/An%20investigation%20into%20student%20performance%20prediction.pdf pdf en http://umpir.ump.edu.my/id/eprint/41898/2/An%20investigation%20into%20student%20performance%20prediction%20using%20regularized%20logistic%20regression_ABS.pdf Kurniadi, Felix Indra and Dewi, Meta Amalya and Murad, Dina Fitria and Rabiha, Sucianna Ghadati and Awanis, Romli (2023) An investigation into student performance prediction using regularized logistic regression. In: 2023 IEEE 9th International Conference on Computing, Engineering and Design, ICCED 2023. 9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023 , 7 - 8 November 2023 , Kuala Lumpur. pp. 1-6. (197271). ISBN 979-835037012-6 (Published) https://doi.org/10.1109/ICCED60214.2023.10425782
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
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Kurniadi, Felix Indra
Dewi, Meta Amalya
Murad, Dina Fitria
Rabiha, Sucianna Ghadati
Awanis, Romli
An investigation into student performance prediction using regularized logistic regression
description The problem of university dropout poses a significant challenge to education systems worldwide, affecting administrators, teachers, and students. Early identification and intervention strategies are crucial for addressing this issue. In addition, advances in machine learning have paved the way for more accurate predictions of student performance. This paper investigates the use of regularization techniques, specifically Lasso (L1) and Ridge (L2) regularization, within logistic regression models to improve the accuracy of performance prediction. This research's dataset was obtained from the Binus Online Learning platform at Bina Nusantara University, with a focus on the Information System study program between 2020 and 2021. The results reveal that logistic regression with regularization achieves a high level of accuracy, recall, and precision in predicting student performance. The findings contribute to the development of an early warning system to identify at-risk students, enabling timely intervention and support.
format Conference or Workshop Item
author Kurniadi, Felix Indra
Dewi, Meta Amalya
Murad, Dina Fitria
Rabiha, Sucianna Ghadati
Awanis, Romli
author_facet Kurniadi, Felix Indra
Dewi, Meta Amalya
Murad, Dina Fitria
Rabiha, Sucianna Ghadati
Awanis, Romli
author_sort Kurniadi, Felix Indra
title An investigation into student performance prediction using regularized logistic regression
title_short An investigation into student performance prediction using regularized logistic regression
title_full An investigation into student performance prediction using regularized logistic regression
title_fullStr An investigation into student performance prediction using regularized logistic regression
title_full_unstemmed An investigation into student performance prediction using regularized logistic regression
title_sort investigation into student performance prediction using regularized logistic regression
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/41898/1/An%20investigation%20into%20student%20performance%20prediction.pdf
http://umpir.ump.edu.my/id/eprint/41898/2/An%20investigation%20into%20student%20performance%20prediction%20using%20regularized%20logistic%20regression_ABS.pdf
http://umpir.ump.edu.my/id/eprint/41898/
https://doi.org/10.1109/ICCED60214.2023.10425782
_version_ 1822924594092179456
score 13.23243

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