Student Engagement Predictive Analytics Platform

Design a sophisticated predictive analytics system using TensorFlow and Keras that can forecast student dropout risks, engagement levels, and academic performance trajectories. The model should integrate multiple data sources including attendance records, assignment submissions, discussion forum interactions, and learning management system logs. Implement advanced feature engineering, handle class imbalance, and create an interpretable machine learning pipeline with model explainability features.