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<p>In the current aviation industry’s demand for excellent service personnel surge, this paper emphasizes the importance of physical training for college aviation service personnel to improve the image of individuals and companies. To this end, this study is oriented to the employment needs of aviation enterprises, creates the “9C cooperation” physical training and nurturing model, and establishes a detailed set of physical training quality evaluation index system. Taking University S as a case study, the data were collected through questionnaires, and factor analysis was applied to comprehensively assess the quality of training and nurturing of aviation service talents. Combined with the IPA analysis model, this paper further explores the correlation between corporate satisfaction and importance and the physical training system. The study results showed that the total variance contribution of the four main factors to the quality of physical training and nurturing was as high as 91.33%, while the overall score of the quality of training and nurturing was 3.153. The importance and satisfaction of enterprises to the quality of form training are distributed in a high range, showing that the training system needs to be further improved. Therefore, colleges and universities need to refine the teaching objectives and contents to ensure that the quality of physical training for aviation service talents can meet the actual needs of aviation enterprises and promote the industry’s overall development.</p>
</abstract>

In the current aviation industry’s demand for excellent service personnel surge, this paper emphasizes the importance of physical training for college aviation service personnel to improve the image of individuals and companies. To this end, this study is oriented to the employment needs of aviation enterprises, creates the “9C cooperation” physical training and nurturing model, and establishes a detailed set of physical training quality evaluation index system. Taking University S as a case study, the data were collected through questionnaires, and factor analysis was applied to comprehensively assess the quality of training and nurturing of aviation service talents. Combined with the IPA analysis model, this paper further explores the correlation between corporate satisfaction and importance and the physical training system. The study results showed that the total variance contribution of the four main factors to the quality of physical training and nurturing was as high as 91.33%, while the overall score of the quality of training and nurturing was 3.153. The importance and satisfaction of enterprises to the quality of form training are distributed in a high range, showing that the training system needs to be further improved. Therefore, colleges and universities need to refine the teaching objectives and contents to ensure that the quality of physical training for aviation service talents can meet the actual needs of aviation enterprises and promote the industry’s overall development.

The construction of physical training and nurturing system for aviation service talents under the background of deep learning

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In the current aviation industry’s demand for excellent service personnel surge, this paper emphasizes the importance of physical training for college aviation...

The construction of physical training and nurturing system for aviation service talents under the background of deep learning

Publicado en línea: 26 feb 2024

Páginas: -

Recibido: 04 ene 2024

Aceptado: 14 ene 2024

DOI: https://doi.org/10.2478/amns-2024-0385

Palabras clave
Factor analysis, IPA model, Total variance contribution rate, Comprehensive evaluation, 9C cooperation

© 2024 Weina Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The construction of physical training and nurturing system for aviation service talents under the background of deep learning

Publicado en línea: 26 feb 2024

Páginas: -

Recibido: 04 ene 2024

Aceptado: 14 ene 2024

DOI: https://doi.org/10.2478/amns-2024-0385

Palabras claveFactor analysis, IPA model, Total variance contribution rate, Comprehensive evaluation, 9C cooperation

© 2024 Weina Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Factor analysis, IPA model, Total variance contribution rate, Comprehensive evaluation, 9C cooperation