Enhancing Academic Trajectories: A Machine Learning Framework for Optimized Student Placement

Abstract

In the context of increasing enrollments and concerns over student retention in higher education, this study introduces a machine learning framework designed to optimize student placement in academic programs. Addressing the challenges posed by the surge in student numbers and the complexities of matching student profiles to suitable programs, the proposed methodology leverages data analytics to predict student success and mitigate dropout rates. The framework facilitates the creation of student profiles and employs machine learning techniques to align incoming students with optimal academic paths, with the goal of fostering a more effective and personalized educational environment.