Student Performance Predictive Pipeline with Machine Learning

Design a comprehensive automated data pipeline that ingests student academic records from multiple sources (LMS, assessment platforms, attendance systems) and uses machine learning to predict at-risk students with 85%+ accuracy. Create a workflow that automatically generates personalized intervention recommendations, triggers email notifications to counselors, and updates student support tracking dashboards in real-time. Include error handling, data anonymization protocols, and a modular architecture that can scale across different educational institutions.