<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, the 15 original variables of the Airpak-based influence factor scale for art and design talent training were first determined, and the five-level scoring method was applied to score the 15 influence factors. Then, we used factor analysis to factorize the data, standardize the sample data, construct a coordinate system with common factors, and use the projection of each variable in the coordinate system instead of the original variables to construct the correlation coefficient matrix R. Then, we solved the correlation coefficient matrix between each variable, selected the number of common factors, and calculated the eigenvectors of the factor loading matrix A to construct the influence factor analysis model. Finally, the model is used to analyze the weight of each influencing factor of Airpak-based art and design talent cultivation and to build an innovative path for talent cultivation. In terms of students’ own factors, the influence weight of learning enthusiasm on talent cultivation is 0.48, and the influence weights of learning achievement and design talent are 0.43 and 0.545, respectively; in terms of teaching level factors, the influence weights of teachers’ teaching ability, professional level and design experience on talent cultivation are 0.44, 0.5 and 0.37 respectively. The research of this paper has an important reference and reference value for the cultivation of art and design talents.</p>
</abstract>

In this paper, the 15 original variables of the Airpak-based influence factor scale for art and design talent training were first determined, and the five-level scoring method was applied to score the 15 influence factors. Then, we used factor analysis to factorize the data, standardize the sample data, construct a coordinate system with common factors, and use the projection of each variable in the coordinate system instead of the original variables to construct the correlation coefficient matrix R. Then, we solved the correlation coefficient matrix between each variable, selected the number of common factors, and calculated the eigenvectors of the factor loading matrix A to construct the influence factor analysis model. Finally, the model is used to analyze the weight of each influencing factor of Airpak-based art and design talent cultivation and to build an innovative path for talent cultivation. In terms of students’ own factors, the influence weight of learning enthusiasm on talent cultivation is 0.48, and the influence weights of learning achievement and design talent are 0.43 and 0.545, respectively; in terms of teaching level factors, the influence weights of teachers’ teaching ability, professional level and design experience on talent cultivation are 0.44, 0.5 and 0.37 respectively. The research of this paper has an important reference and reference value for the cultivation of art and design talents.

Airpak-based art and design talent training innovation model framework

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Airpak-based art and design talent training innovation model framework"}

In this paper, the 15 original variables of the Airpak-based influence factor scale for art and design talent training were first determined, and the...

Airpak-based art and design talent training innovation model framework

Publicado en línea: 11 oct 2023

Páginas: -

Recibido: 05 ene 2023

Aceptado: 21 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00590

Palabras clave
Factor analysis, Loading matrix, Airpak, Coefficient matrix, Art design

© 2023 Hui Qiao et al., published by Sciendo

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

Airpak-based art and design talent training innovation model framework

Publicado en línea: 11 oct 2023

Páginas: -

Recibido: 05 ene 2023

Aceptado: 21 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00590

Palabras claveFactor analysis, Loading matrix, Airpak, Coefficient matrix, Art design

© 2023 Hui Qiao et al., published by Sciendo

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

Palabras clave
Factor analysis, Loading matrix, Airpak, Coefficient matrix, Art design