Research on College Students Course PerformanceAnalysis Based on Decision Tree Algorithm andIndustry-Education Integration Model

The integration of industry and education is a growing trend in higher education that has gained attention due to its potential tobridge the gap between academic learning and industry demands. One of the challenges in this integration is analyzing andimproving students’ course performance to ensure the alignment of education with industry expectations. Traditional methods,such as manual assessments and simple statistical analyses, often fail to capture the complexity and nuances of studentperformance. These approaches can be limited in their predictive power and adaptability to varying student profiles. This paperproposes a novel approach utilizing decision tree algorithms combined with an industry-education integration model for courseperformance analysis. The decision tree model efficiently handles categorical and continuous data, making it a suitable methodfor classifying and predicting student performance based on various academic and socio-economic factors. Furthermore, byincorporating an industry-education integration model, we introduce a framework that considers the evolving needs of theworkforce and aligns educational outputs with these requirements. Experimental results show that the decision tree algorithmsignificantly outperforms traditional methods in terms of prediction accuracy and interpretability. The proposed approach notonly enhances the accuracy of course performance analysis but also provides actionable insights that can be leveraged byeducational institutions to improve curricula and teaching strategies in line with industry needs.