Student Success Early Warning System

Design a sophisticated early warning system using machine learning to predict student dropout risks and academic challenges. Develop a Python pipeline that integrates multiple data sources including academic records, engagement metrics, demographic information, and psychological assessments. Implement advanced ensemble machine learning models using scikit-learn, create a probabilistic risk scoring system, and develop an automated intervention recommendation engine. Include model explainability, continuous learning capabilities, and comprehensive performance monitoring.