Automated Student Performance Trend Analysis Pipeline

Design a Laravel-based data pipeline that automatically aggregates student performance metrics from multiple database sources, performs statistical analysis using machine learning libraries like PHP-ML, and generates predictive reports identifying at-risk students. The system should integrate with existing student information systems, process data hourly, and create customizable alert mechanisms for academic intervention. Include robust error handling, logging, and support for multiple data formats including CSV, JSON, and SQL database connections.