Improving the prediction of school dropout with the support of the semi-supervised learning approach
School dropout is a phenomenon characterized by being influenced by several variables. This research used Machine Learning techniques, especially in the context of the semi-supervised learning strategy, to predict the risk of dropout in undergraduate courses at a Brazilian higher education instituti...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | Brasil |
| Institución: | Sociedade Brasileira de Computação (SBC) |
| Repositorio: | Brazilian Journal of Information Systems |
| Idioma: | inglés |
| OAI Identifier: | oai:journals-sol.sbc.org.br:article/2852 |
| Acceso en línea: | https://journals-sol.sbc.org.br/index.php/isys/article/view/2852 |
| Access Level: | acceso abierto |
| Palabra clave: | School Dropout Machine Learning Semi-supervised Learning Educational Data Mining |
| Sumario: | School dropout is a phenomenon characterized by being influenced by several variables. This research used Machine Learning techniques, especially in the context of the semi-supervised learning strategy, to predict the risk of dropout in undergraduate courses at a Brazilian higher education institution. Two phases of experiments were conducted, the first using Feature Selection techniques and the second applying a semi-supervised learning strategy to improve performance metrics collected from the increase in the number of instances of students labeled as Graduated. As a main result, we obtained a model capable of classifying dropout with 90% accuracy and 86% Macro-F1. |
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