Improving the prediction of school dropout with the support of the semi-supervised learning approach

• Autor(a) principal: Cardoso Melo, Eduardo
• Data de Publicação: 2023
• Outros Autores: Sumika Hojo de Souza, Fernanda
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Journal of Information Systems
• Texto Completo: https://journals-sol.sbc.org.br/index.php/isys/article/view/2852
• topic: School Dropout; Machine Learning; Semi-supervised Learning; Educational Data Mining; School Dropout; Machine Learning; Semi-supervised Learning; Educational Data Mining

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.