Integrating Predictive Modeling and Machine Learning for Class Success Forecasting in Creative Education Sectors

Forecasting student success in creative education presents unique challenges due to the non-linear learning paths, subjective assessments, and emotional dynamics inherent to these disciplines. This paper develops a comprehensive, risk-driven predictive modeling framework leveraging machine learning techniques tailored specifically to the creative education sector. By integrating diverse data sources such as academic records, engagement metrics, and qualitative feedback, the framework enables early identification of students at risk of dropout. It further outlines methods for embedding predictive risk scores into personalized educational interventions including mentoring, counseling, and adaptive learning to support retention. Ethical considerations surrounding data privacy, algorithmic bias, and transparency are critically addressed to ensure responsible implementation. The proposed framework offers practical benefits for educators and institutions aiming to reduce dropout rates and enhance student success while advancing theoretical understanding of predictive analytics in education. Future research directions include improving model interpretability, incorporating multi-modal data, and expanding predictive targets beyond dropout to encompass broader educational outcomes.