Prediciendo el rendimiento académico de estudiantes de pregrado en una universidad destacada de Perú: Una aproximación con herramientas de Machine Learning

• Autores: Salas, Fabio, Caldas, Josué
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Pontificia Universidad Católica del Perú
• Repositorio: PUCP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.pucp.edu.pe:20.500.14657/199343
• Enlace del recurso: https://revistas.pucp.edu.pe/index.php/educacion/article/view/28596/26338; https://repositorio.pucp.edu.pe/index/handle/123456789/199343; https://doi.org/10.18800/educacion.202401.M003
• Nivel de acceso: acceso abierto
• Materia: Academic performance; Machine Learning; Higher Education; Peru; Desempenho Acadêmico; Aprendizado de Máquina; Ensino Superior; Rendimiento Académico; Educación Superior; Perú; https://purl.org/pe-repo/ocde/ford#5.03.00

Despite improved higher education accessibility in low and middle-income countries (LMICs), challenges persist in student drop-out, especially for socio-economically disadvantaged students. While machine learning models have enhanced our understanding of this challenge by predicting academic performance, many studies overlook LMIC-specific institutional factors or focus on specific courses, limiting their generalizability and policy uses. To address these issues, the authors compiled a comprehensive database using administrative and census data to predict undergraduate academic performance at the Pontifical Catholic University of Peru (PUCP). The study found that the most effective models were tree-based ensembles, particularly Random Forest, with key predictors including prior secondary school performance and university admission test scores. They present a high-performing model using only ten features that can predict future academic performance and potentially aid in reducing student drop-out at PUCP.