Design and Implementation of a Blockchain-Based Student Competency Modeling and Intelligent Employment Matching System

Lianchao Zhang

doi:10.71222/w1gsk185

Authors

Lianchao Zhang Graduate School, University of the East, Manila, Philippines Author

DOI:

https://doi.org/10.71222/w1gsk185

Keywords:

blockchain, competency modeling, employment matching, dynamic weighting, similarity fusion, higher education

Abstract

Addressing the persistent challenges and growing disconnect between higher education talent cultivation strategies and the dynamic employment needs of modern enterprises, this paper designs and implements an innovative blockchain-based student competency modeling and intelligent employment matching system. Currently, the recruitment landscape is plagued by unreliable student competency data, static and one-sided competency profiles, and notably low accuracy in employment matching. To overcome these critical limitations, the proposed system adopts a robust consortium blockchain architecture. This decentralized approach ensures the highly reliable storage, immutability, and mutual recognition of multi-source heterogeneous competency data across various educational and corporate stakeholders. Furthermore, the system constructs a comprehensive, multi-dimensional competency index framework that meticulously encompasses academic achievements, practical abilities, and essential professional qualities. To accurately reflect evolving student capabilities, we propose a dynamically weighted competency profile model that seamlessly integrates the analytic hierarchy process (AHP), the entropy weighting method, and a time decay function. Additionally, a multi-dimensional intelligent matching mechanism is introduced, effectively integrating attributes, dynamic weights, and trend similarity to optimize candidate selection. Comprehensive system implementation and rigorous evaluation results demonstrate that the proposed architecture performs exceptionally well in terms of functional applicability, performance efficiency, and data security. Most importantly, its matching accuracy and interpretability are significantly superior to those of traditional models. Ultimately, this research provides a highly effective new technical path and a practical, scalable solution for ensuring accurate talent supply and demand matching between universities and enterprises.

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