Adaptive Learning Progress Tracking Pipeline

Design a Python-based automated data pipeline using pandas and scikit-learn that tracks individual student learning trajectories across multiple online courses. The system should dynamically generate personalized learning recommendations by analyzing completion rates, quiz scores, time spent, and knowledge retention metrics. Implement machine learning models to predict potential learning gaps and automatically trigger intervention alerts for at-risk students. Include robust error handling, logging mechanisms, and a Flask dashboard for administrative visualization.