Mechanism of Disciplinary Adaptability-Driven Differences in Advanced Mathematics Academic Performance: Empirical Analysis Based on Multidisciplinary Groups and Optimization Pathways for Teaching

This study investigates the mechanisms through which disciplinary backgrounds (Chemistry, Mechanical Engineering, Economics, Big Data, and Computer Science) influence academic performance in advanced mathematics via empirical analysis. Using a sample of 250 students from a university, methods such as ANOVA, correlation analysis, and multiple linear regression were applied. Key findings include: (1)Significant differences in academic performance across disciplines (F=7.789,p<0.01), with STEM majors (Computer Science, Big Data, Mechanical Engineering) outperforming Chemistry and Economics majors (mean difference > 6.5 points); (2) Core predictors of performance include pre-university mathematics scores (β=0.771,p<0.01), course engagement (β=5.623,p<0.01), and high school mathematics scores (β=0.089,p=0.011); (3) Learning interest positively correlates with performance (r=0.406), yet regression analysis reveals its indirect impact through engagement. Based on these results, strategies such as discipline-specific content optimization, blended teaching, and dynamic feedback mechanisms are proposed to support differentiated instruction.