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© FEGLININ ISSN 2594-2298

| Año 8, No 32, enero - marzo 2025 |

• Educar sobre el uso equilibrado de redes sociales.
• Identificar estudiantes en riesgo para ofrecerles apoyo adicional.
Este enfoque estructurado proporciona una base para entender y mitigar las causas del bajo
rendimiento académico, contribuyendo a una educación más inclusiva y efectiva.
REFERENCIAS BIBLIOGRAFICAS

• García, P. (2019). Social factors influencing academic performance. Higher Education Review,
47(2), 112-123.
• González, A., & Pérez, M. (2021). Digital surveys in educational research. Journal of Modern
Education, 11(1), 36-49.
• López, E., & Martínez, F. (2020). Data science for predicting academic performance: A review
of models in education. International Journal of Educational Technology, 19(3), 200-215.
• Martínez, R., & López, P. (2020). Random forests in academic prediction models. Educational
Data Mining Journal, 18(4), 76-89.
• Pérez, D., & Ramos, M. (2022). The effect of stress and sleep on academic performance in
university students. Journal of Student Wellbeing, 21(1), 58-70.
• Rodríguez, L., & Torres, V. (2018). Machine learning in education: A review. Journal of Learning
Analytics, 25(2), 54-67.
• Sánchez, J., González, F., & Ruiz, A. (2019). Predicting academic success with machine learning:
A comparison of models. Data Science in Education, 16(2), 120-135
• Smith, J. (2017). Impact of personal factors on academic performance. Journal of Educational
Psychology, 32(4), 45-56.

Prefijo DOI: 10.70417

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