Can Simple Balancing Algorithms Improve School Dropout Forecasting? The Case of the State Education Network of Espírito Santo, Brazil

Guilherme Armando de Almeida Pereira () and Kiara de Deus Demura
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Guilherme Armando de Almeida Pereira: Department of Economics, Federal University of Espírito Santo, Vitória 29075-910, Brazil
Kiara de Deus Demura: Education Center, Jones dos Santos Neves Institute, Vitória 29052-015, Brazil

Forecasting, 2025, vol. 7, issue 4, 1-19

Abstract: This study evaluates the effect of simple data-level balancing techniques on predicting school dropout across all state public high schools in Espírito Santo, Brazil. We trained Logistic Regression with LASSO (LR), Random Forest (RF), and Naive Bayes (NB) models on first-quarter data from 2018–2019 and forecasted dropouts for 2020, with additional validation in 2022. Facing strong class imbalance, we compared three balancing methods—RUS, SMOTE, and ROSE—against models trained on the original data. Performance was assessed using accuracy, sensitivity, specificity, precision, F1, AUC, and G-mean. Results show that the imbalance severely harmed RF and NB trained without balancing, while Logistic Regression remained more stable. Overall, balancing techniques improved most metrics: RUS and ROSE were often superior, while SMOTE produced mixed results. Optimal configurations varied by year and metric, and RUS and ROSE made up most of the best combinations. Although most configurations benefited from balancing, some decreased performance; therefore, we recommend systematic testing of multiple balancing strategies and further research into SMOTE variants and algorithm-level approaches.

Keywords: student dropout forecasting; educational data mining; highly imbalanced classification problems; students at risk of dropping out (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2025
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