Analysis of Student Academic Performance Through Machine Learning Methods in The Field of Pedagogy

This article explores the application of machine learning algorithms to analyze, predict, and personalize the educational process based on students' academic performance. Through statistical and computational methods, various student-related attributes were analyzed, with the Random Forest algorithm identified as the most accurate predictive model. The study led to the development of an intelligent system for diagnostic evaluation, personalized approaches, and automated pedagogical recommendations. The findings highlight the significant potential of artificial intelligence tools in enhancing the effectiveness of education.