Automated Student Performance Tracking Pipeline with Machine Learning

Design a comprehensive data pipeline that integrates student assessment data from multiple learning management systems, uses machine learning to predict at-risk students, and automatically generates personalized intervention recommendations. The solution should handle data from diverse sources like Canvas, Blackboard, and custom school databases, with real-time processing capabilities. Include error handling for data inconsistencies, a scalable architecture using microservices, and a dashboard for administrative visualization.