Dynamic Student Assessment Recommendation Engine

Create a Python-powered recommendation system that analyzes individual student performance data to suggest personalized learning resources, additional practice materials, and adaptive assessment strategies. Utilize collaborative filtering and machine learning algorithms to generate individualized learning paths. Implement a modular architecture that can integrate with existing student information systems and provide real-time recommendations based on ongoing performance metrics.