Research on the evaluation of undergraduate students' core competencies in the context of industry-education integration.

Under the dual impetus of industrial upgrading and higher education quality enhancement, it has become a common consensus to construct a student competency evaluation tool that can reflect the entire "teaching-learning-application" chain. However, existing studies remain insufficient in terms of system integrity and quantitative operability. Guided by the CIPP evaluation model (Context-Input-Process-Product), this study follows the logical progression of "context-input-process-output." Through a combination of literature review, expert interviews, and the Delphi method, an initial set of indicators was developed. The Analytic Hierarchy Process (AHP) was then employed to determine indicator weights, and a fuzzy comprehensive evaluation approach was integrated to construct the quantitative model. The results indicate that the established system effectively balances process monitoring and outcome orientation, emphasizing university-industry collaboration, authentic learning contexts, and ability transferability, while demonstrating strong interpretability and diagnostic value. The final framework includes 4 primary indicators, 11 secondary indicators, and 68 tertiary indicators. The expert authority coefficients for the two Delphi rounds were 0.840 and 0.845, respectively, with Kendall's coordination coefficients of 0.182 and 0.244. The AHP consistency test yielded CR < 0.1, confirming reliability. Using a sample of 132 students from the 2019 cohort of the Mechanical Engineering program at a "Double First-Class" university, model application results showed that 79.6% of students achieved an overall competency level of "good" or above. Among the first-level dimensions, the expected values of process evaluation and input evaluation outperformed those of context evaluation and output evaluation, suggesting the need to further strengthen institutional reputation building and graduate quality feedback mechanisms. The findings demonstrate that the proposed indicator system and evaluation model can effectively mitigate ambiguity and subjectivity in competency assessment. It possesses high applicability and promotional value in supporting teaching quality diagnostics, talent training program optimization, and deep university-industry collaboration.