Minería de Datos Educativos en la Predicción de la Deserción Estudiantil: Tendencias, Oportunidades y Retos
DOI:
https://doi.org/10.5753/rbie.2024.3559Keywords:
Deserción Estudiantil, Predicción de Deserción, Minería de Datos Educativos, Revisión Sistemática de LiteraturaAbstract
Actualmente nos enfrentamos a pérdidas académicas, sociales y económicas asociadas a la deserción estudiantil. Varios estudios han aplicado técnicas de minería de datos a conjuntos de datos educativos para comprender los perfiles de abandono y reconocer a los estudiantes en situación de riesgo. Con el fin de identificar las características contextuales (niveles, modalidades y sistemas educativos), técnicas (tareas, categorías de algoritmos y herramientas) y de datos (tipos, cobertura y volumen) relacionadas con estos estudios, se realizó una revisión sistemática de la literatura, considerando el abandono institucional y de curso. De repositorios reconocidos internacionalmente, se seleccionaron artículos que mostraban, entre otras características, una mayor explotación de datos académicos, demográficos y económicos de estudiantes universitarios, a partir de técnicas de clasificación por comités de árboles de decisión. Además de que no se identificaron estudios de países subdesarrollados entre los seleccionados, se observaron deficiencias en la aplicación de los modelos predictivos y en la puesta a disposición de los gestores académicos de sus predicciones, lo que sugiere que los esfuerzos y el potencial de la mayoría de estos estudios están infrautilizados en la práctica educativa.
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Derechos de autor 2024 Miriam Pizzatto Colpo, Tiago Thompsen Primo, Marilton Sanchotene de Aguiar, Cristian Cechinel
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
