A model for predicting dropout of higher education students
Higher education institutions are becoming increasingly concerned with the retention of their students. This work is motivated by the interest in predicting and reducing student dropout, and consequently in reducing the financial losses of said institutions. Based on the characterization of the drop...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-03-01
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| Series: | Data Science and Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666764924000341 |
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| author | Anaíle Mendes Rabelo Luis Enrique Zárate |
| author_facet | Anaíle Mendes Rabelo Luis Enrique Zárate |
| author_sort | Anaíle Mendes Rabelo |
| collection | DOAJ |
| description | Higher education institutions are becoming increasingly concerned with the retention of their students. This work is motivated by the interest in predicting and reducing student dropout, and consequently in reducing the financial losses of said institutions. Based on the characterization of the dropout problem and the application of a knowledge discovery process, an ensemble model is proposed to improve dropout prediction. The ensemble model combines the results of three models: Logistic Regression, Neural Networks, and Decision Tree. As a result, the model can correctly classify 89% of the students as enrolled or dropped and accurately identify 98.1% of dropouts. When compared with the Random Forest ensemble method, the proposed model demonstrates desirable characteristics to assist management in proposing actions to retain students. |
| format | Article |
| id | doaj-art-444a4c8d0580403ab7d9f008a2d44858 |
| institution | Kabale University |
| issn | 2666-7649 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Data Science and Management |
| spelling | doaj-art-444a4c8d0580403ab7d9f008a2d448582025-02-08T05:01:24ZengKeAi Communications Co. Ltd.Data Science and Management2666-76492025-03-01817285A model for predicting dropout of higher education studentsAnaíle Mendes Rabelo0Luis Enrique Zárate1Department of Computer Science, Applied Computational Intelligence Laboratory-LICAP, Pontifical Catholic University of Minas Gerais, Belo Horizonte, Minas Gerais, 30535-901, BrazilCorresponding author.; Department of Computer Science, Applied Computational Intelligence Laboratory-LICAP, Pontifical Catholic University of Minas Gerais, Belo Horizonte, Minas Gerais, 30535-901, BrazilHigher education institutions are becoming increasingly concerned with the retention of their students. This work is motivated by the interest in predicting and reducing student dropout, and consequently in reducing the financial losses of said institutions. Based on the characterization of the dropout problem and the application of a knowledge discovery process, an ensemble model is proposed to improve dropout prediction. The ensemble model combines the results of three models: Logistic Regression, Neural Networks, and Decision Tree. As a result, the model can correctly classify 89% of the students as enrolled or dropped and accurately identify 98.1% of dropouts. When compared with the Random Forest ensemble method, the proposed model demonstrates desirable characteristics to assist management in proposing actions to retain students.http://www.sciencedirect.com/science/article/pii/S2666764924000341Educational data miningDropout predictionRegression logisticDecision treeNeural networks |
| spellingShingle | Anaíle Mendes Rabelo Luis Enrique Zárate A model for predicting dropout of higher education students Data Science and Management Educational data mining Dropout prediction Regression logistic Decision tree Neural networks |
| title | A model for predicting dropout of higher education students |
| title_full | A model for predicting dropout of higher education students |
| title_fullStr | A model for predicting dropout of higher education students |
| title_full_unstemmed | A model for predicting dropout of higher education students |
| title_short | A model for predicting dropout of higher education students |
| title_sort | model for predicting dropout of higher education students |
| topic | Educational data mining Dropout prediction Regression logistic Decision tree Neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S2666764924000341 |
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