2022
Autores
Ferreira, P; Malheiro, B; Silva, M; Borges Guedes, P; Justo, J; Ribeiro, C; Duarte, A;
Publicação
EDULEARN Proceedings - EDULEARN22 Proceedings
Abstract
2022
Autores
Ferreira, P; Malheiro, B; Silva, M; Borges Guedes, P; Justo, J; Ribeiro, C; Duarte, A;
Publicação
EDULEARN Proceedings - EDULEARN22 Proceedings
Abstract
2022
Autores
Silva, M;
Publicação
Journal of Artificial Intelligence and Technology
Abstract
[No abstract available]
2022
Autores
Costa, T; Coelho, L; Silva, MF;
Publicação
Advances in Medical Technologies and Clinical Practice
Abstract
2022
Autores
Teixeira, S; Arrais, R; Dias, R; Veiga, G;
Publicação
Procedia Computer Science
Abstract
2022
Autores
Da Silva, DEM; Pires, EJS; Reis, A; Oliveira, PBD; Barroso, J;
Publicação
FUTURE INTERNET
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 Tras-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 Fl-Score of 81% in the final test set, concluding that students' marks somehow incorporate their living conditions.
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