Research Article Open Access

Understanding a Profile of the Participants of the Exame Nacional do Ensino Médio (ENEM), Brazil, in the Year 2019, Through Data Analysis

Thiago Oliveira de Souza1, Angélica Félix de Castro1 and Amanda Gondim de Oliveira1
  • 1 Department of Computer Science, Federal University of the Semi-Arid Region (UFERSA), Mossoró, Brazil

Abstract

In Brazil, there is the Exame Nacional do Ensino Médio (ENEM), which allows people to enter the University to complete their graduation course. It is a selection that takes place throughout Brazil and is the main way for a student to enter the University. Understanding the candidates' profiles is interesting to know if there is a pattern: Which courses are most chosen by women, for example, or if the North Region has a different interest than the South Region. Understanding education throughout the national territory becomes important and necessary. The main objective of this study is to verify if there are relations between some characteristics of the candidates and their performance in the ENEM. With the help of the Python language and its libraries, it was possible to find some factors that influence the performance of the exam participants and categorize some characteristics of their profiles. In general terms, it was noticed that both age and gender of the participants are not deterministic factors for their performance; that the candidates from private schools obtained higher results in all ENEM tests; that the schooling of the parents of the participants tends to influence the result obtained in the grades, among other conclusions.

Journal of Computer Science
Volume 19 No. 7, 2023, 888-899

DOI: https://doi.org/10.3844/jcssp.2023.888.899

Submitted On: 17 March 2023 Published On: 26 July 2023

How to Cite: de Souza, T. O., Castro, A. F. & de Oliveira, A. G. (2023). Understanding a Profile of the Participants of the Exame Nacional do Ensino Médio (ENEM), Brazil, in the Year 2019, Through Data Analysis. Journal of Computer Science, 19(7), 888-899. https://doi.org/10.3844/jcssp.2023.888.899

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Keywords

  • Data Science
  • ENEM
  • Python
  • Pandas
  • Matplotlib
  • Seaborn