Forecasting Students Dropout: A UTAD University Study

Diogo E. Moreira da Silva, Eduardo J. Solteiro Pires, Arsénio Reis, Paulo B. de Moura Oliveira and João Barroso
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Diogo E. Moreira da Silva: ECT–UTAD Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, 5000-811 Vila Real, Portugal
Eduardo J. Solteiro Pires: ECT–UTAD Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, 5000-811 Vila Real, Portugal
Arsénio Reis: ECT–UTAD Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, 5000-811 Vila Real, Portugal
Paulo B. de Moura Oliveira: ECT–UTAD Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, 5000-811 Vila Real, Portugal
João Barroso: ECT–UTAD Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, 5000-811 Vila Real, Portugal

Future Internet, 2022, vol. 14, issue 3, 1-14

Abstract: In Portugal, the dropout rate of university courses is around 29%. Understanding the reasons behind such a high desertion rate can drastically improve the success of students and universities. This work applies existing data mining techniques to predict the academic dropout mainly using the academic grades. Four different machine learning techniques are presented and analyzed. The dataset consists of 331 students who were previously enrolled in the Computer Engineering degree at the Universidade de Trás-os-Montes e Alto Douro (UTAD). The study aims to detect students who may prematurely drop out using existing methods. The most relevant data features were identified using the Permutation Feature Importance technique. In the second phase, several methods to predict the dropouts were applied. Then, each machine learning technique’s results were displayed and compared to select the best approach to predict academic dropout. The methods used achieved good results, reaching an F1-Score of 81% in the final test set, concluding that students’ marks somehow incorporate their living conditions.

Keywords: students dropout; Random Forest; XGBoost; CatBoost; artificial neural network; permutation feature importance (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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