Evaluating Strategies to Predict Student Dropout of a Bachelor's Degree in Computer Science

  • Dayane Perez Bravo Universidade Federal do Paraná (UFPR)
  • Marco Antonio Zanata Alves Universidade Federal do Paraná (UFPR) http://orcid.org/0000-0003-2440-2664
  • Leandro Augusto Ensina Universidade Federal do Paraná (UFPR) / Universidade Tecnológica Federal do Paraná (UTFPR)
  • Luiz Eduardo Soares de Oliveira Universidade Federal do Paraná (UFPR)


The Brazilian Higher Education Census has revealed that the dropout rate among higher education students in Brazil exceeds 50% starting from the fifth year. This high rate results in several problems related to the wastage of resources invested by both the society and the students. Therefore, universities need to develop strategies to prevent student dropout and minimize these problems. However, predicting student dropout involves detecting patterns and predicting them over a large amount of data collected yearly from thousands of students. Given the scale and volume of data involved in dropout prediction, machine learning emerges as a powerful technique to automate the identification of these students. The objective of this paper is to identify students who are prone to dropping out based on the academic history of Bachelor’s Degree in Computer Science students at an unpaid public university in Brazil. We engineered four datasets based on the semester in which the students are enrolled. These datasets are designed to simulate the academic scenario and individual characteristics of the students available up to the prediction moment. Besides, we propose three feature models to identify the best scenario. Our method could identify the students most likely to drop out and the main features that contributed to the respective decision. Using only the information from the disciplines taken by the students proved to be the best feature model. When using these features with Gradient-Boosting, the F1-Score performance ranged between 69% and 85%, depending on the dataset.

Palavras-chave: data mining, feature engineering, pattern recognition, student evasion


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BRAVO, Dayane Perez; ZANATA ALVES, Marco Antonio; ENSINA, Leandro Augusto; DE OLIVEIRA, Luiz Eduardo Soares. Evaluating Strategies to Predict Student Dropout of a Bachelor's Degree in Computer Science. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 11. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 1-8. ISSN 2763-8944. DOI: https://doi.org/10.5753/kdmile.2023.232763.