Empirical Study on Employment Trend Prediction for Engineering Management Undergraduates in Local Universities

Kaihe Shi

doi:10.6918/IJOSSER.202505_8(5).0047

Authors

Kaihe Shi

DOI:

https://doi.org/10.6918/IJOSSER.202505_8(5).0047

Keywords:

Undergraduate employment in engineering management; Hausdorff fractional-order grey model; data-driven; employment trend prediction; local higher education institutions.

Abstract

This study develops a Hausdorff Fractional Grey Model (HFGM(1,1)) to address traditional models' limitations in predicting engineering management undergraduates' employment trends. Integrating Hausdorff fractional accumulation and particle swarm optimization, the model dynamically adapts to nonlinear dynamics under the "new information priority" principle. Empirical validation using Tianjin university data demonstrates superior accuracy (1.84% MAPE) over conventional models. It quantifies coupled impacts of construction cycles, curriculum design, and practical training, providing actionable insights for academic reforms and tiered career guidance. The framework aids policymakers in optimizing enrollment strategies and industry-academia collaboration. Future research should expand regional validation and hybrid modeling approaches for enhanced predictive precision in higher education management.

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