Mineração de dados educacionais na identificação do perfil de alunos com risco de evasão em escola técnica pública com base na trajetória acadêmica

Abstract

Public Technical Schools in the State of São Paulo, known as ETECs, seek to align their educational programs with labor market demands through courses that combine theory and practice. These institutions face challenges related to school dropout, which can be examined through institutional academic data. The case of ETEC Raposo Tavares illustrates this context: only 43.59% of the students who entered the Technical Course in Chemistry in 2023 completed the program in 2024, a percentage below the institutional target of 65%. In this context, Educational Data Mining (EDM) makes it possible to explore these data and identify patterns associated with students at risk of dropout. This study aims to apply Educational Data Mining to identify profiles of students at risk of dropout in the Technical Course in Chemistry at ETEC Raposo Tavares, based on their academic trajectory, and to support school management in the development of educational strategies. The database used in the computational experiments contains information on 185 students enrolled from the first to the third module in 2023 and 2024. The data were extracted from the New Academic System (NSA) and supplemented with records from class council minutes. The experiments followed the phases of the Knowledge Discovery in Databases process: Data Selection, Data Preprocessing, Data Transformation, Educational Data Mining, and Knowledge Interpretation and Evaluation. Logistic Regression, Random Forest, and XGBoost were applied, and Random Forest achieved the best performance. Based on its results, the school management team identified three profiles: High Academic Performance, associated with students who are committed to the course and seek completion; Medium Academic Performance, associated with students who, despite difficulties, seek to complete the course; and Low Academic Performance, associated with a higher risk of dropout. The last profile may be related to social inequalities, lack of family support, vocational misalignment, or a weak sense of institutional identification and belonging. Among the measures selected by the management team, the institutionalization of an internal continuous monitoring protocol stands out. The protocol should connect the indicators to pedagogical practices so that the data can support concrete educational strategies. The study concludes that EDM is a relevant resource for ETEC Raposo Tavares and can support school management in the development of strategies aimed at reducing the risk of dropout.