Comprehensive Student Success Prediction Framework

Design a sophisticated machine learning framework that predicts student success using a holistic approach. Integrate multiple data sources including academic records, socio-economic indicators, psychological assessments, and engagement metrics. Implement advanced feature engineering, ensemble modeling techniques, and create a comprehensive risk scoring system with actionable intervention strategies. Include model interpretability components to understand key predictive factors.