Evasão nos cursos de graduação em Computação no Brasil

• Ano de defesa: 2024
• Autor(a) principal: Alvim, Ícaro Vasconcelos
• Orientador(a): Bittencourt, Roberto Almeida
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual de Feira de Santana
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação
• Departamento: DEPARTAMENTO DE CIÊNCIAS EXATAS
• País: Brasil
• Palavras-chave em Português: Evasão; Educação superior; Computação; INEP; Censo da Educação Superior
• Palavras-chave em Inglês: Dropout; Higher education; Computing; INEP; Brazilian Higher Education Census
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://tede2.uefs.br:8080/handle/tede/1846

Dropping out of higher education has several negative impacts: social, economic, institutional and personal. Its effects are even more serious in the area of information and communication technology, where there is a growing demand for higher education professionals. This work describes dropout rates in undergraduate programs in the field of Computing in Brazil between 2015 and 2019 based on data provided by the National Institute of Educational Studies and Research Anísio Teixeira (INEP). The demographic characterization of students in these programs was carried out and potential differences in the dropout rate between different interest groups were measured. The results show that the largest difference is between students who depend and those who do not depend on financial loans. In addition, significant differences were also found between students from public and private institutions, between fellowship and non-scholarship students, and between students who study in different daily shifts. Finally, smaller differences were found between students who receive and those who do not receive social support, between affirmative action and non-affirmative action students, and between students in face-to-face and distance courses. Another contribution of this work was the measurement of potential differences in the dropout rate between different demographic groups for students from public and private institutions and also for students from Computer Science and Computing Teaching degrees. Results indicate that, for students from public institutions, significant differences were found between those who are enrolled in face-to-face and distance learning courses, between students who study in different daily shifts, between those who receive and those who do not receive social support, and between fellowship and non-fellowship students. Results also indicate that for students studying Computer Science, significant differences were found between students who study in different daily shifts, between affirmative action and non-affirmative action students, and between those who receive and those who do not receive social support. Finally, significant differences were found between fellowship and non-fellowship students for both the Computer Science degree and the Computing Teaching degree. Based on the results found, we conclude that there is little difference in dropout between different undergraduate program types and that factors related to students’ socioeconomic issues play an important role in their dropout. However, factors such as gender and race/ethnicity, when observed in isolation, did not substantially impact student dropout in computing programs, contrasting with the results presented in studies carried out in other countries.