Early warning evaluation model of dynamic adjustment of higher vocational majors based on AHP

FengLin Qu

doi:10.1117/12.2648651

27 September 2022 Early warning evaluation model of dynamic adjustment of higher vocational majors based on AHP

FengLin Qu

Proceedings Volume 12345, International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2022); 1234517 (2022) https://doi.org/10.1117/12.2648651
Event: 2022 International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2022), 2022, Qingdao, China

Abstract

The construction of professional dynamic adjustment early warning evaluation model in higher vocational colleges is the need to promote and improve the construction and management of the national "double high plan", the need to complete the construction objectives and tasks of the "double high plan" with high quality, and the practical need to innovate and enrich the performance index evaluation system of higher vocational colleges. Based on AHP analytic hierarchy process, this paper designs the mathematical model of dynamic adjustment and early warning evaluation of Higher Vocational Majors, which is monitored from six first-class indicators: student source and scale, school enterprise cooperation, professional teaching guarantee conditions, professional teaching and construction, scientific research and social services, training quality and social reputation, and calculates the comprehensive ranking of Majors Based on the data of higher vocational talent training status, which is an important basis for professional dynamic adjustment. The model is used to measure the specialty of Chengdu Vocational and technical college. The calculation results are consistent with the actual situation of the University, which proves that the evaluation model is effective and can be popularized.

Citation Download Citation

FengLin Qu "Early warning evaluation model of dynamic adjustment of higher vocational majors based on AHP", Proc. SPIE 12345, International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2022), 1234517 (27 September 2022); https://doi.org/10.1117/12.2648651

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KEYWORDS

Data modeling

Scientific research

Bismuth

Chromium

Lithium

Mathematical modeling

MATLAB

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