Revolutionizing educational decision-making: a robust machine learning mechanism for predicting student performance

Abstract Machine learning has become an essential component across various domains, including the education sector. Accurately predicting students’ academic performance plays a critical role for teachers and school administrators—not only in enhancing the quality of education but also in influencing educational outcomes. In this study, we propose an innovative system capable of predicting student achievement with high accuracy. We analyze a data set containing student-related features such as gender, race/ethnicity, parental education level, participation in breakfast programs, test preparation, participation in courses, computer science scores, and literacy skills. A supervised machine learning approach, specifically the random forest algorithm, was employed to build the prediction model. Redundant features were eliminated to reduce complexity and computational cost. Additionally, a comparative analysis was conducted to demonstrate the effectiveness of the proposed method. The findings of this research have the potential to transform educational decision-making and support a more data-driven and effective strategy for improving academic outcomes.