Academic performance and sociocultural conditions of students from a rural educational institution in Peru

• Autores: Ruelas, Donia, Ruelas, Elio, Carrera, Marta, Aceituno, Miguel
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Instituto Universitario de Innovación Ciencia y Tecnología Inudi Perú
• Repositorio: Revista Innova Educación
• Lenguaje: español
• OAI Identifier: oai:ojs2.revistainnovaeducacion.com:article/1050
• Enlace del recurso: https://revistainnovaeducacion.com/index.php/rie/article/view/1050
• Nivel de acceso: acceso abierto
• Materia: analítica del aprendizaje; aprendizaje automático; estudiantes de secundaria; rendimiento académico; zona rural; learning analytics; machine learning; secondary school students; academic performance; rural area; analítica da aprendizagem; aprendizado de máquina; estudantes do ensino secundário; desempenho acadêmico

Educational institutions collect and manage information about their students in order to support decision-making aimed at improving academic performance and the overall achievement of their community. The objective of this research was to identify findings on academic performance considering sociocultural factors throughout secondary education. The study followed the KDD process (Knowledge Discovery in Databases) and applied machine learning algorithms for data mining. As a result, two main findings were identified: first, the family environment and the level of student responsibility directly influenced academic performance; second, five student profiles were detected based on these factors: (1) favorable family environment and high level of responsibility, associated with outstanding performance; (2) favorable environment and low responsibility, with limited performance; (3) unfavorable environment and high responsibility, which partially compensates for family disadvantages; (4) unfavorable environment and low responsibility, linked to the lowest performance; and (5) intermediate profiles with mixed combinations of family conditions and responsibility. These findings, obtained through machine learning techniques, provide knowledge about the situation of the educational institution, facilitating more assertive decisions for the continuous improvement of academic performance in the rural sector of Peru. These findings derive from data collected in a specific rural institution; therefore, they may vary in studies conducted in urban contexts or other countries.