Multimodal Machine Learning for Student Retention Prediction in the College of Engineering

Student retention analysis and prediction supports interventions in higher education. We present a web-based tool to predict first-year and multi-year retention in the College of Engineering at Tennessee Technological University. The system integrates socio-demographic attributes, academic performance indicators, and advisement notes as predictive features. Structured and NLP-derived features are fused in a hybrid architecture with XGBoost one and multi-term predictions, respectively, with explainability using SHAP to identify influential factors for retention prediction.