Background
In recent years, higher education institutions have increasingly adopted digital learning platforms, generating large volumes of student interaction data. Learning Management Systems (LMS), online assessments, and attendance tracking tools provide detailed insights into student behavior.
Despite this abundance of data, many institutions still rely on traditional evaluation methods, which often fail to identify at-risk students early. This gap highlights the need for intelligent systems that can analyze behavioral patterns and predict academic outcomes proactively.
Advances in artificial intelligence, particularly machine learning, have enabled the development of predictive models capable of identifying hidden patterns in educational data. These technologies offer the potential to enhance decision-making processes, improve student retention, and personalize learning experiences.
