Modelo de Predicción Para Evaluar La Adaptabilidad de Los Estudiantes En Educación Virtual: Un Enfoque Basado En Machine Learning

This study uses machine learning (ML) models to predict the adaptability of students in virtual education environments. Using variables such as gender, education level, financial conditions and access to technology, a predictive model was built that classifies students according to their adaptability. The random Forest (RF) model achieved an accuracy of 88.8%, providing useful tools for educators and administrators to anticipate needs and adapt pedagogical strategies, promoting a more efficient and personalized learning experience.