A multivariate analysis of the early dropout using classical machine learning and local interpretable model-agnostic explanations
Student dropout rates can have a significant negative impact on both the development of educational institutions and the personal growth of students. Consequently, many institutions are focused on identifying key factors that contribute to dropout and implementing strategies to mitigate them. This...
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| Format: | Article |
| Language: | English |
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Can Tho University Publisher
2024-10-01
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| Series: | CTU Journal of Innovation and Sustainable Development |
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| Online Access: | https://ctujs.ctu.edu.vn/index.php/ctujs/article/view/1171 |
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| author | Thanh Hai Nguyen Phuong Le Tuyen Thanh Thi Nguyen Anh Kim Su |
| author_facet | Thanh Hai Nguyen Phuong Le Tuyen Thanh Thi Nguyen Anh Kim Su |
| author_sort | Thanh Hai Nguyen |
| collection | DOAJ |
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Student dropout rates can have a significant negative impact on both the development of educational institutions and the personal growth of students. Consequently, many institutions are focused on identifying key factors that contribute to dropout and implementing strategies to mitigate them. This study aims to predict student dropout rates using classical machine learning algorithms while analyzing the key factors influencing these outcomes in higher education. The dataset includes demographic, socioeconomic, and academic information from various sources. Additionally, the study leverages the Local Interpretable Model-Agnostic Explanations (LIME) model to provide insights into the predictions, offering a clearer understanding of the factors driving dropout decisions. This knowledge is crucial for identifying influential factors and, more importantly, enhancing early intervention strategies and policies in educational settings, ultimately reducing dropout rates.
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| format | Article |
| id | doaj-art-5f2fcaffe1354be0bbf10638d99e2640 |
| institution | OA Journals |
| issn | 2588-1418 2815-6412 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Can Tho University Publisher |
| record_format | Article |
| series | CTU Journal of Innovation and Sustainable Development |
| spelling | doaj-art-5f2fcaffe1354be0bbf10638d99e26402025-08-20T02:16:49ZengCan Tho University PublisherCTU Journal of Innovation and Sustainable Development2588-14182815-64122024-10-0116Special issue: ISDS10.22144/ctujoisd.2024.327A multivariate analysis of the early dropout using classical machine learning and local interpretable model-agnostic explanationsThanh Hai NguyenPhuong LeTuyen Thanh Thi NguyenAnh Kim Su Student dropout rates can have a significant negative impact on both the development of educational institutions and the personal growth of students. Consequently, many institutions are focused on identifying key factors that contribute to dropout and implementing strategies to mitigate them. This study aims to predict student dropout rates using classical machine learning algorithms while analyzing the key factors influencing these outcomes in higher education. The dataset includes demographic, socioeconomic, and academic information from various sources. Additionally, the study leverages the Local Interpretable Model-Agnostic Explanations (LIME) model to provide insights into the predictions, offering a clearer understanding of the factors driving dropout decisions. This knowledge is crucial for identifying influential factors and, more importantly, enhancing early intervention strategies and policies in educational settings, ultimately reducing dropout rates. https://ctujs.ctu.edu.vn/index.php/ctujs/article/view/1171Dropout Prediction, Machine learning, Explanation |
| spellingShingle | Thanh Hai Nguyen Phuong Le Tuyen Thanh Thi Nguyen Anh Kim Su A multivariate analysis of the early dropout using classical machine learning and local interpretable model-agnostic explanations CTU Journal of Innovation and Sustainable Development Dropout Prediction, Machine learning, Explanation |
| title | A multivariate analysis of the early dropout using classical machine learning and local interpretable model-agnostic explanations |
| title_full | A multivariate analysis of the early dropout using classical machine learning and local interpretable model-agnostic explanations |
| title_fullStr | A multivariate analysis of the early dropout using classical machine learning and local interpretable model-agnostic explanations |
| title_full_unstemmed | A multivariate analysis of the early dropout using classical machine learning and local interpretable model-agnostic explanations |
| title_short | A multivariate analysis of the early dropout using classical machine learning and local interpretable model-agnostic explanations |
| title_sort | multivariate analysis of the early dropout using classical machine learning and local interpretable model agnostic explanations |
| topic | Dropout Prediction, Machine learning, Explanation |
| url | https://ctujs.ctu.edu.vn/index.php/ctujs/article/view/1171 |
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