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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| Format: | Conference or Workshop Item |
| Language: | English English |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| 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 |
| _version_ | 1811138512784719872 |
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| 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 |
| collection | UMP |
| 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. |
| first_indexed | 2024-09-25T03:51:23Z |
| format | Conference or Workshop Item |
| id | UMPir41898 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-09-25T03:51:23Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | UMPir418982024-08-30T00:14:54Z 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 |
| 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 |
| title | 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_short | An investigation into student performance prediction using regularized logistic regression |
| title_sort | investigation into student performance prediction using regularized logistic regression |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| 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 |
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