Predictive Student Engagement Risk Assessment Engine

Construct a sophisticated machine learning pipeline that predicts student disengagement and dropout risks using multi-dimensional data analysis. The system should integrate behavioral, academic, and contextual signals, utilizing advanced ensemble learning techniques like gradient boosting and neural networks. Design a modular architecture that allows real-time risk scoring and generates actionable intervention recommendations for educational administrators.