Classification of Peruvian Elementary School Students with Low Achievement Problems Using Clustering Algorithms and ERCE Evaluation

• Autores: Rojas-Salvatierra, Nancy, Parodi-Roman, Lucas, Montalvo, Peter
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/676031
• Enlace del recurso: http://hdl.handle.net/10757/676031
• Nivel de acceso: acceso embargado
• Materia: Agglomerative Clustering; Artificial Intelligence; Clustering; Education; K-Means; Low Performance; Machine Learning; Students

At present there are several problems that affect students and their academic performance such as low socioeconomic status that can cause lack of resources both in their homes and in the school. In addition to psychological and personal problems in which students can be involved. According to various national and international examinations the academic level in Peru is quite low because the problems mentioned above are difficult to identify, it is not possible to propose a viable solution, which is why we propose a Machine Learning model based on Clustering algorithms such as KMeans, Birch and Aglomerative that manage to group students by the most relevant characteristics or disadvantages they present.