Análise da participação feminina nos cursos da área de computação da rede federal

Abstract

This study analyzes female participation in Computing courses at the Federal Education Network of Brazil, based on the microdata provided on the Nilo Peçanha Platform from 2017 to 2023. The analysis was conducted in two stages. Initially, a descriptive quantitative approach was adopted, using Power BI to examine the data from all Computer Science courses in the Federal Network. Next, only the data from the Federal Institute of Pernambuco (IFPE) was considered, in order to conduct a predictive analysis using Machine Learning techniques, with the aim of identifying variables capable of predicting student dropout in the Computing courses of this institution. The results indicate a continuous growth in female participation, reaching 37.64% at IFPE in 2023, a percentage higher than the national average. However, dropout rates are critical among low-income and brown students. The Random Forest algorithm showed the best performance in predicting dropout risk, with an accuracy of 70.32% and a recall of 84.67%. It is concluded that socioeconomic and racial factors have a greater predictive weight on dropout rates than gender alone. Thus, retention policies should prioritize social vulnerability, aiming to ensure the continued presence of women in the field of Computing.