Dynamic Learning Path Recommendation Engine

Design a machine learning recommendation system using pandas and scikit-learn that analyzes student performance data and generates personalized curriculum progression paths. The system should consider historical performance metrics, learning speed, subject difficulty, and individual knowledge gaps. Implement adaptive algorithm that dynamically adjusts recommendations in real-time based on continuous assessment results. Include a Flask-based API endpoint for retrieving personalized recommendations and visualize recommendation confidence scores.