Big Data-Driven Vocational Undergraduate Talent Training and Social Needs Adaptability Analysis and Collaborative...

Area of Science:

• Educational Technology
• Data Science
• Sociology of Education

Background:

• The mismatch between undergraduate talent cultivation and social demand is a significant challenge in higher education.
• Current social needs analysis and talent training models in vocational undergraduate programs require enhancement.
• Increasingly severe employment situations highlight the need to improve the quality and employability of graduates.

Purpose of the Study:

• To propose a big data-driven method for analyzing adaptability and mining collaborative paths between vocational undergraduate talent cultivation and social demand.
• To address the prominent issue of student employability in the context of societal needs.
• To develop a framework for vocational undergraduate talent training that aligns with current social demands.

Main Methods:

• Utilizing big data analytics for adaptability analysis between talent cultivation and social demand.
• Implementing clustering models and mining methods to analyze talent training and social demand.
• Conducting collaborative path mining to identify optimal training strategies.

Main Results:

• The analysis revealed that basic adaptability constitutes a higher proportion in personnel training and social adaptation.
• Professional competence demonstrated a higher trust value in cluster analysis, indicating its importance.
• The proposed method provides a framework for aligning educational output with labor market needs.

Conclusions:

• A big data-driven approach can effectively bridge the gap between vocational education and social demand.
• Enhancing professional competence and basic adaptability is crucial for improving graduate employability.
• The study offers a valuable model for vocational undergraduate talent training programs in China and beyond.