AI ANALYSIS REPORT

Random Forest-Based Prediction Model for Student Satisfaction with School-Enterprise Co-developed Digital Teaching Resources in Vocational Undergraduate Education

Vocational education faces challenges in effectively integrating industry demands and digital transformation. While school-enterprise co-developed digital teaching resources are crucial, their effectiveness from a student perspective remains insufficiently evaluated, leading to structural contradictions between development and application. This study surveyed 525 vocational undergraduate students regarding their demographics, resource usage, and satisfaction across 15 dimensions. A Random Forest algorithm was employed for classification modeling to predict student satisfaction and identify key influencing factors. The model was compared against Decision Tree, Logistic Regression, and Support Vector Machine baselines. The Random Forest model achieved strong predictive performance with 84.76% accuracy, 87.3% precision, 93.2% recall, and an AUC-ROC of 0.892, significantly outperforming baseline models. Feature importance analysis revealed that practical skills improvement, industry-technology alignment, and virtual simulation experience were the most influential predictors, collectively accounting for 48% of total importance. The findings provide empirical evidence and actionable recommendations for optimizing school-enterprise collaboration mechanisms. Key recommendations include prioritizing hands-on learning, ensuring regular content updates aligned with industry practices, investing in platform usability, and establishing effective feedback channels for continuous resource optimization.