Intelligent Student Engagement Risk Prediction System

Develop an automated predictive modeling framework that integrates multiple data sources (learning management system logs, attendance records, assessment scores, engagement metrics) to generate early warning signals for potential student disengagement. Implement machine learning models that can dynamically adjust risk thresholds, provide personalized intervention recommendations, and generate automated communication triggers for academic advisors and support staff.