An instructional emperor pigeon optimization (IEPO) based DeepEnrollNet for university student enrolment prediction and retention recommendation

Academic institutions face increasing challenges in predicting student enrollment and managing retention. A comprehensive strategy is required to track student progress, predict future course demand, and prevent student churn across various disciplines. Institutions need an effective method to predict student enrollment while addressing potential churn. The existing approaches are often inadequate in handling both numerical and textual data, limiting the ability to provide personalized retention strategies. We propose an innovative framework that combines deep learning with recommender systems for student enrollment prediction and churn prevention. The framework integrates advanced preprocessing techniques for both numeric and textual data. Feature extraction is performed with statistical measures for numeric data, and advanced text techniques like GloVe embeddings, Latent Dirichlet Allocation (LDA) for topic modeling, and SentiWordNet for sentiment analysis. A weighted feature fusion approach combines these features, and the optimal features are selected using the Pythagorean fuzzy AHP with a Hybrid Optimization approach, specifically the Instructional Emperor Pigeon Optimization (IEPO). The DeepEnrollNet model, a hybrid CNN-GRU-Attention QCNN architecture, is used for enrollment prediction, while Deep Q-Networks (DQN) are applied to generate actionable retention recommendations. This comprehensive methodology improves predictive accuracy for student enrolment and provides tailored strategies to enhance retention by addressing both text and numeric data in a unified framework. The DeepEnrollNet has the minimum MSE of 0.218978, MSRE of 0.216445, a NMSE of 0.232453, RMSE of 0.23213, and MAPE of 0.218754.