Architecture to Support Automatic Grading Processes in Programming Teaching

Julio C. Caiza, Jose M. Del Alamo

Resumen


Resumen: La calificación automática de tareas de programación es un tema importante dentro del campo de la innovación educativa que se enfoca en mejorar las habilidades de programación de los estudiantes y en optimizar el tiempo que el profesorado dedica a ello. Uno de los principales problemas vigentes está relacionado con la diversidad de criterios para calificar las tareas de programación. El presente trabajo propone e implementa una arquitectura, basada en el concepto de orquestación de servicios, para soportar varios procesos de calificación automática de tareas de programación. Esto es obtenido a través de las características de modularidad, extensibilidad y flexibilidad que la arquitectura provee al proceso de calificación. La arquitectura define como pieza clave un elemento llamado Grading-submodule, el mismo que provee un servicio de evaluación del código fuente considerando un criterio de calificación. La implementación se ha llevado a cabo sobre la herramienta Virtual Programming Lab; y los resultados demuestran la factibilidad de realización, y la utilidad tanto para el profesorado como para los estudiantes.

Abstract: Automatic grading of programming assignments is an important topic in academic research. It aims at improving students’ programming skills and optimizing the time of teaching staff. One important gap is related to the diversity of criteria to grade programming assignments. This work proposes and implements an architecture, based on the services orchestration concept, to support many kinds of grading process of programming assignments. It is achieved due architecture’s features including modularity, extensibility, and flexibility. The cornerstone of the architecture is a new software component named Grading-submodule, which provides of an evaluation service for the source code considering a grading criterion. The implementation has been done on Virtual Programming Lab. Results show workability, and uselfulness for teaching staff and students.


 



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