Student Performance Predictive Model Using Advanced Machine Learning

Design a comprehensive predictive analytics framework to forecast student academic performance using multi-dimensional data sources. Create a machine learning pipeline that integrates historical academic records, engagement metrics, demographic information, and learning platform interactions. The model should provide a probabilistic risk assessment for student dropout, include feature importance ranking, and generate actionable intervention recommendations with at least 85% accuracy. Include cross-validation strategies and explain how feature engineering will address potential data bias in educational datasets.