Real-Time Student Performance Anomaly Detection System

Design a Node.js-based anomaly detection system for educational institutions that monitors student performance in real-time. Implement statistical and machine learning algorithms to identify unexpected performance variations, potential academic dishonesty, and early signs of student disengagement. Create a modular architecture supporting multiple data sources, with configurable alert mechanisms and comprehensive reporting features. Include advanced statistical techniques like z-score normalization and isolation forest algorithms.