Reconstruction and Optimization of University Employment Guidance Course Model under Emerging Industries: A Collaborative Education Framework

Abstract

The rapid evolution of emerging industries, characterized by digital transformation and new business paradigms, has created a pressing need to reform university employment guidance courses to better align with dynamic labor market demands. Traditional models often lack adaptability and fail to integrate interdisciplinary knowledge, resulting in a mismatch between graduate competencies and employer expectations. This study proposes and validates a collaborative education framework to reconstruct and optimize the employment guidance course model, emphasizing synergy between universities, enterprises, and policymakers. Through a multi-site controlled experiment involving 1,872 final-year undergraduates across three Chinese universities, we implemented a framework incorporating top-level policy design, school-enterprise cooperation, curriculum content optimization infused with emerging industry knowledge, diversified teaching methods (project-based learning, case analysis, hybrid online-offline approaches), and a multi-evaluation system combining traditional assessments with innovation metrics. Results demonstrate that experimental group students achieved a 28.7% increase in employability index compared to 9.3% in control groups (p<0.001), with particularly strong gains in adaptive competencies: cross-domain synthesis (=1.83vs.0.61), iterative prototyping (=2.15 vs.0.72), and stakeholder negotiation (=1.94 vs.0.55). Enterprise satisfaction ratings for experimental group interns were 2.4 times higher (7.9/10 vs. 3.3/10). The framework offers a scalable blueprint for higher education institutions seeking to modernize career guidance programs in an era of rapid technological and economic change.