Prácticas de autorregulación del aprendizaje en Ciencias de la Computación: Una revisión sistemática
DOI:
https://doi.org/10.71068/ss2rft29Palabras clave:
Autorregulación del aprendizaje, Ciencias de la Computación, Educación superior, Estrategias de aprendizaje, Pensamiento computacionalResumen
Los estudiantes universitarios de áreas afines a ciencias de la computación, como la programación, enfrentan desafíos como resolver problemas complejos, comprender conceptos abstractos y la organización lógica de soluciones. Ante estas exigencias, la autorregulación del aprendizaje se reconoce como una competencia necesaria para planificar, monitorear y reflexionar sobre el propio proceso cognitivo, contribuyendo así al desarrollo del pensamiento computacional y una mejora sostenida del rendimiento académico. Esta revisión sistemática analizó 59 estudios publicados entre 2021 y 2025, consultados en las plataformas académicas ERIC, Open Alex, Scopus, Scilit y Google Scholar, con el objetivo de identificar estrategias de autorregulación del aprendizaje en este campo, dada la escasez de estudios recientes que las analizan de forma estructurada. Se identificaron 402 estrategias, clasificadas en tres categorías principales: cognitivas (35 %), metacognitivas (34 %) y de gestión de recursos (31 %). Las cognitivas incluyen estrategias como descomposición de problemas, depuración de errores, elaboración y práctica. Las metacognitivas abarcaron planificación, monitoreo y reflexión. Las de gestión de recursos comprendieron la organización del tiempo, la regulación de la motivación, trabajo colaborativo, uso de plataformas tecnológicas y gamificación.
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Derechos de autor 2025 Paulo Cesar Coronado Sánchez, Carlos Javier Mosquera Suárez (Autor/a)
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