Abstract
In the digital age, the deep integration of information technology into higher education has transformed student work in engineering colleges. Traditional experience-based management fails to meet dynamic student needs, necessitating a shift to data-driven approaches through systematic modeling. This study identifies core dimensions via literature review, establishes a factor interaction system, and constructs a student work effectiveness analysis model using multiple linear regression. It converts the abstract system into a quantifiable framework with variables like policy support, management mechanisms, and professional competence. Using structural equation modeling and regression analysis, the study verifies each variable's positive impact on work effectiveness. Quantitative research reveals how social factors, organizational resources, professional capabilities, student characteristics, and informatization levels drive work effectiveness, offering a scientific basis for resource allocation. The digital environment requires overcoming fragmented management, while engineering students' strong professionalism and practical needs demand personalized, targeted work. The proposed model provides an operable evaluation system, guiding the optimization of university-college management. It enables precise decision-making by identifying key factors, constructs dynamic monitoring via longitudinal data, and clarifies variable causal relationships through path analysis. Ultimately, model-driven precision services support cultivating high-quality, innovative engineering talents adaptable to the digital era.