Brazilian society suffers permanent financial damage when students of higher education courses disassociate with universities without completing the courses in which they were enrolled, mainly in which there was a contribution of public resources. The academic management of an IES comprises several activities. Within the Brazilian federal universities, there are several socio-economic policies and programs to assist and provide support for actions that seek to minimize the dropout of enrolled students, maximize the number of students who graduated on adequate course time, as well as improvements in the process of learning. One of the main challenges of higher education is a dropout, a situation that occurs when students fill the vacancies and dissociate themselves from universities without completing the course in which they have enrolled. According to OECD (2013), the average annual cost per undergraduate student in Brazilian public education was US$ 13,539.90. This value shows that evasion implies significant financial pain for the country. To solve this problem, we propose a machine learning model using genetic algorithm and decision tree based on Educational Data Mining (EDM), to identify the students at-risk to abandon the course in which they are enrolled.
- Post in Medium of the Data Hackers Community
- Related Works from the Systematic Review
- Dataset in Portuguese (Original)
- Dataset in English (Translated)
Santos, G. A. S., A. L. Bordignon, S. L. G. Oliveira, D. B. Haddad, D. N. Brandão, and K. T. Belloze. "A Brief Review about Educational Data Mining applied to Predict Student’s Dropout." Anais da V Escola Regional de Sistemas de Informação do Rio de Janeiro, Nova Friburgo, 2018. SBC, 2018, pp.86 - 91. DOI: https://doi.org/10.5753/ersirj.2018.4660
Santos, G. A. S., A. L. Bordignon, D. B. Haddad, D. N. Brandão, L. Tarrataca, and K. T. Belloze. "Data Warehouse Educacional: Uma visão sobre a Evasão no Ensino Superior". In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 34. , 2019, Fortaleza. Anais do XXXIV Simpósio Brasileiro de Banco de Dados. Porto Alegre: Sociedade Brasileira de Computação, nov. 2019 . p. 235-240. DOI: https://doi.org/10.5753/sbbd.2019.8829.
G. A. S. Santos, K. T. Belloze, L. Tarrataca, D. B. Haddad, A. L. Bordignon and D. N. Brandao, "EvolveDTree: Analyzing Student Dropout in Universities," 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), Niterói, Brazil, 2020, pp. 173-178, DOI: https://doi.org/10.1109/IWSSIP48289.2020.9145203.
- Gustavo Alexandre
- Diego Brandao
- Luiz Tarrataca
- Diego Haddad
- Alex Laier
- Kele Belloze
My Orcid