Abstract
The dropout of university students has been a factor of concern for educational institutions, affecting various aspects such as the institution’s reputation and funding and rankings. For this reason, it is essential to identify which students are at risk . In this study, algorithms based on decision trees and random forests are proposed to solve these problems using real data from 331 students from the University of Trásos-Montes and Alto Douro. In this work with these learning algorithms together with the training strategies , we managed to obtain an 89% forecast of students who may abandon their studies based on the evaluations of both semesters related to the first year and personal data.
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Silva, B., Pires, E.J.S., Reis, A., de Moura Oliveira, P.B., Barroso, J. (2021). Students Drop Out Trends: A University Study. In: Reis, A., Barroso, J., Lopes, J.B., Mikropoulos, T., Fan, CW. (eds) Technology and Innovation in Learning, Teaching and Education. TECH-EDU 2020. Communications in Computer and Information Science, vol 1384. Springer, Cham. https://doi.org/10.1007/978-3-030-73988-1_36
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DOI: https://doi.org/10.1007/978-3-030-73988-1_36
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