Content-Based Recommendation System for Programming Judges using Natural Language Processing and Deep Learning

• Autores: Julca-Mejia, Wilson, Paucar-Curasma, Herminio
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad Nacional Mayor de San Marcos
• Repositorio: Revistas - Universidad Nacional Mayor de San Marcos
• Lenguaje: español
• OAI Identifier: oai:ojs.csi.unmsm:article/25802
• Enlace del recurso: https://revistasinvestigacion.unmsm.edu.pe/index.php/rpcsis/article/view/25802
• Nivel de acceso: acceso abierto
• Materia: Programming Online Judges; Recommender Systems; Natural Language Processing; Deep Learning; Jueces de Programación en línea; Sistemas de Recomendación; Procesamiento de Lenguaje Natural; Aprendizaje Profundo

In the field of education and technology companies, online judges play an important role in the development of programming skills because on these platforms students must solve challenges using specific programming languages. However, the sheer number of coding challenges available can be overwhelming for students, leading to frustration and loss of interest. To resolve this situation, recommender systems can be an effective solution. However, programming judges have not delved far enough into this area. Therefore, this research focused on evaluating six artificial intelligence techniques through a cloud-based architecture for the prediction of the level of difficulty from the statements of the problems to be coupled to a recommendation system. To validate the experiments, a real CodeChef programming judge was used and the experiments were evaluated through statistical tests. The results indicated that the BERT model is the best for predicting the level of the problems, which helps the recommendation system to improve the learning experience of the students in the online programming judges.