Adaptive Learning Path Generator with Machine Learning

Design a Python-based recommendation system using scikit-learn that dynamically generates personalized learning paths for students based on their historical performance, learning style, and assessment data. The system should utilize collaborative filtering and predictive modeling to suggest curriculum modules, recommended study materials, and intervention strategies. Include a robust feature engineering pipeline that can handle sparse student data, with a minimum of 85% prediction accuracy and built-in explainability metrics.