Estrategias para la Extracción de Patrones Secuenciales en la Enseñanza de Algoritmos: Una Comparación entre Algoritmos Clásicos y Metaheurísticas Evolutivas
DOI:
https://doi.org/10.5753/rbie.2025.5119Keywords:
Minería de Patrones Secuenciales, Metaheurísticas Evolutivas, Personalización de la Enseñanza de ProgramaciónAbstract
El descubrimiento de patrones secuenciales en el comportamiento de los estudiantes en entornos de enseñanza de programación en línea es esencial para la personalización del aprendizaje y la optimización del proceso educativo. Con la transformación digital en la enseñanza, las plataformas orientadas al aprendizaje de algoritmos han ganado protagonismo, pero aún carecen de un secuenciamiento personalizado de actividades que ayude en la progresión educativa. En este escenario, la aplicación de la Minería de Patrones Secuenciales (MPS) puede revelar patrones de aprendizaje y desafíos recurrentes enfrentados por los alumnos, convirtiéndose en una herramienta valiosa para la personalización de la experiencia de aprendizaje. Así, el presente estudio compara dos enfoques de MPS: algoritmos clásicos y metaheurísticas evolutivas, para evaluar sus eficacias en la detección de patrones. La investigación analiza las interacciones de 313 alumnos en un entorno de enseñanza de algoritmos, observando el desempeño de los enfoques en cuanto a precisión, costo computacional y aplicabilidad. Los algoritmos clásicos se muestran eficientes en bases de datos más pequeñas, mientras que las metaheurísticas evolutivas revelan patrones complejos con mayor eficacia en grandes volúmenes de datos. Los resultados indican que ambos enfoques pueden beneficiar el entorno de enseñanza, ayudando al profesor a anticipar señales de desmotivación e intervenir de forma proactiva para mantener al alumno interesado y activo en la plataforma. Como contribución, se demuestra que tanto los algoritmos clásicos como las metaheurísticas evolutivas pueden generar insights valiosos, como la necesidad de revisar un problema específico o proporcionar más ejemplos prácticos a los alumnos.
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Derechos de autor 2025 Djefferson Smith Santos Maranhão, Carlos de Salles Soares Neto
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
