Use of Bayesian networks in Brazil high school educational database: analysis of the impact of COVID-19 on ENEM in Pará between 2019 and 2022 - Santos, Sandio Maciel Dos; Silva, Marcelino Silva da; França Lobato, Fábio Manoel; Francês, Carlos Renato Lisboa (Frontiers in Big Data, 2025)
This study examines the impact of the COVID-19 pandemic on academic performance and student participation in the National High School Exam (ENEM) in the state of Pará, Brazil, focusing on the interaction between socioeconomic factors, access to technology, and regional disparities. The research employed a mixed-methods approach, analyzing quantitative data from ENEM results (2020–2022) and qualitative interviews with educators and students. The findings indicate that the pandemic exacerbated pre-existing educational inequalities, particularly affecting low-income students and those enrolled in public schools. The highest dropout rates were recorded among students with a family income of up to one minimum wage, highlighting the barriers posed by limited access to technology and infrastructure for remote learning. A statistical analysis revealed a 20% increase in scores among students with access to computers and the Internet, particularly in private schools. The study also found significant regional differences across Pará's mesoregions, with Marajó and Southeast Pará facing more persistent challenges in reducing dropout rates compared to the Metropolitan Region of Belém. These results underscore the urgent need for region-specific public policies that address disparities in educational resources, including targeted investments in digital infrastructure and teacher training for remote education. The study concludes that comprehensive support programs, including psychological assistance for students, are essential for building a more resilient and equitable educational system capable of withstanding future crises.