abstract
- The use of artificial intelligence and machine learning allows us to define better strategies to investigate the general performance of each student and establish predictive strategies, which is why it represents an excellent early warning system, allowing the identification of students at risk of failing subjects in advance and help them. In this study, we used artificial intelligence and the techniques K-Nearest Neighbors, Support Vector Machine (SVM), Naive Bayes, Random Forest Classification to study a sample of 715 high school students from Tecnologico de Monterrey, in Hermosillo and predict the risk of failure per student. Our model showed excellent performance in predicting failed subjects. Our model demonstrates that machine learning is a robust tool for identifying students at risk of failing subjects early and helping them.