CampusVision: A Deep Learning-Based Framework for Enrollment Forecasting of US Higher Education via IPEDS

The ability to forecasting student enrollment accurately is becoming increasingly crucial for universities and colleges because of the demographic changes, the changing educational policies, and the impacts of global events. Nonlinear, dynamic trends of contemporary education data are not adequately captured often by traditional statistical methods, which calls for the need of more advanced data-driven forecasting techniques. The paper discusses CampusVision, a unique deep learning-based forecasting tool that predicts the enrollment trends in U.S. postsecondary institutions short term with the help of IPEDS institutional-level data from the year 2019 to 2023. State-of-the-art sequence modeling methods, especially mechanic LSTM networks, were traversed in depth across numerous configurations, different features of the demographic, and length of time-series input. The models trained using the largest number of demographic features (e.g., race/ethnicity, gender, and residency status) combined with longer input sequences significantly outperformed other tested configurations. The experimental evaluations proved that the CampusVision proposal delivered the most superior outcome performance with the early stages of least prediction errors when deploying a five-year historical window and expanded demographic features MAE: 0.25, RMSE: 0.74 standardized units. The in-depth error analyses strengthened the outstanding performance of the model in the case of diverse and large-enrollment institutions. CampusVision is an accessible, easily replicated, and scalable forecasting pipeline for resource allocation, policy responsiveness, and institutional planning, which institutional researchers and policymakers will be looking into. The initiative will proceed with the integration of additional predictive analytics.