PREDICTIVE ANALYTICS FOR EARLY DETECTION OF LEARNING DIFFICULTIES IN STUDENTS

Abstract

In recent years, data-driven educational technologies have become an essential part of modern smart learning environments. This paper focuses on developing a predictive analytics model aimed at the early detection of learning difficulties among students. The proposed approach uses machine learning techniques and academic performance data to support more accurate and timely decision-making by teachers and school administrators. In this study, multiple datasets were analyzed, including student attendance records, homework completion rates, assessment results, and behavioral indicators collected from digital learning platforms. The model was tested using correlation and regression analysis to identify relationships between academic activity patterns and early signs of learning challenges. The results showed that predictive analytics can reliably detect at-risk students before issues become severe, allowing for earlier intervention and personalized support. The project demonstrates how artificial intelligence can be applied to improve student outcomes, enhance the effectiveness of educational processes, and create more responsive and inclusive learning environments