Predicción de repitencias en estudiantes a nivel escolar usando Machine Learning: una revisión sistemática
DOI:
https://doi.org/10.17162/au.v13i2.1438Palavras-chave:
Métodos de predicción, Repitencia escolar, Machine learning, Deserción escolar, EducaciónResumo
El objetivo principal de la investigación es determinar el estado del arte de la investigación acerca de la Predicción de repitencias en estudiantes a nivel escolar usando Machine Learning. Los resultados obtenidos se han centrado en estudios relacionados a los algoritmos y herramientas de Machine Learning más eficientes para la predicción de repitencia estudiantil. Esto se llevó a cabo mediante una revisión sistemática de la literatura (RSL) en base a Machine Learning, para la predicción de estudiantes con repitencia escolar entre los años 2017-2021. La estrategia de búsqueda identificó 47,490 artículos de bibliotecas digitales como ACM Digital Library, ERIC, Google Scholar, IEEE Xplore, Microsoft Academic, Science Direct y Taylor & Francis Online; de las cuales 90 fueron identificados y seleccionados como adecuados para la revisión. En cuanto a las conclusiones, estas presentan respuestas sobre las categorías de variables más aplicadas en la predicción de repitencia escolar en estudiantes, las métricas utilizadas para evaluar los resultados de la predicción de repitencia escolar, autores con mayor productividad en la predicción de repitencia escolar, y los artículos más citados cuyas discusiones y conclusiones se caracterizan por su objetividad y polaridad en las investigaciones sobre la predicción de estudiantes con repitencia escolar usando Machine Learning.Downloads
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Copyright (c) 2023 Javier Gamboa-Cruzado, Cinthya Y. Alvarez-Cuellar, Shirley Martinez-Medina, Josue Edison Turpo Chaparro, Aníbal Sifuentes Damián, María Rodríguez Kong
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
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