Forecasting Students Dropout: A UTAD University Study

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.

Factores asociados a la deserción estudiantil en el ámbito universitario. Una revisión sistemática 2018-2023

“…En consecuencia, las instituciones deben ofrecer planes de estudio que cautiven a los alumnos, ya que esto favorece a la permanencia, especialmente de aquellos que tienen dudas sobre las metas que desean alcanzar con su formación. En concordancia con esta idea, Moreira et al (2022) explican que el abandono puede ocurrir debido a la falta de madurez de los estudiantes, de ahí que se deban brindar unidades curriculares exitosas y exigentes.…”

“…El abandono puede ocurrir debido a la carencia de madurez de los estudiantes, relacionada con la edad de estos, mientras que para garantizar la finalización de los estudios es menester que la universidad ofrezca unidades curriculares exitosas y exigentes (Moreira et al, 2022).…”

Smart Objects and Technologies for Social Good

“…Diogo E. Moreira da Silva, Eduardo J. Solteiro Pires, Arsénio Reis, Paulo B. de Moura Oliveira, and João Barroso have contributed to the third paper of the Special Issue [3]. The authors mostly predict academic dropout using existing data mining techniques.…”

Course Prophet: A Machine Learning-Based System for Early Prediction of Student Failure in Numerical Methods Course in the Bachelor’s Degree in Engineering at the University of Córdoba, Colombia

“…• artificial neural networks or multilayer perceptron [1][2][3][4][5][6] ensemble methods with different classification methods [1,4];…”

“…• random forest [2][3][4]6], • gradient boosting [3]; extreme gradient boosting (XGBoost) [3,4,6]; • variants of gradient boosting [3,7], namely CatBoost [9] and LightGBM [10]; and • Gaussian processes for classification [6].…”