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

<p>The fusion of industry and education plays a pivotal role in optimizing talent training structures, facilitating the conversion of scientific and technological achievements, and driving the upgrade and high-quality growth of the economy. This study applies a clustering algorithm, a form of data mining, to categorize students based on personality traits, learning capabilities, and career preferences. This categorization aids vocational institutions in selecting suitable corporate partners for training programs. Furthermore, we introduce a collaborative filtering algorithm to establish a bipartite graph that matches the attributes of students and companies. This approach enhances the practical skills of students and supports the strategic integration of industry and education in talent development. After the experimental research, it was found that after the implementation of the senior industry-teaching integration talent cultivation measurement rate, the average score of the higher vocational education improvement status is 4.20, the average score of the industry’s satisfaction with the higher vocational is 3.35, and the average score of the higher vocational talent cultivation characteristics is 4.41, which indicates that the industry-teaching integration education model constructed in this paper can effectively promote the formation of the characteristics of the higher vocational schooling and talent cultivation. By integrating industry teaching, this paper’s parenting model can help change the vocational faculty structure and improve student employment rates.</p>
</abstract>

The fusion of industry and education plays a pivotal role in optimizing talent training structures, facilitating the conversion of scientific and technological achievements, and driving the upgrade and high-quality growth of the economy. This study applies a clustering algorithm, a form of data mining, to categorize students based on personality traits, learning capabilities, and career preferences. This categorization aids vocational institutions in selecting suitable corporate partners for training programs. Furthermore, we introduce a collaborative filtering algorithm to establish a bipartite graph that matches the attributes of students and companies. This approach enhances the practical skills of students and supports the strategic integration of industry and education in talent development. After the experimental research, it was found that after the implementation of the senior industry-teaching integration talent cultivation measurement rate, the average score of the higher vocational education improvement status is 4.20, the average score of the industry’s satisfaction with the higher vocational is 3.35, and the average score of the higher vocational talent cultivation characteristics is 4.41, which indicates that the industry-teaching integration education model constructed in this paper can effectively promote the formation of the characteristics of the higher vocational schooling and talent cultivation. By integrating industry teaching, this paper’s parenting model can help change the vocational faculty structure and improve student employment rates.

Exploration of Talent Cultivation Strategies for Higher Vocational Industry-Teaching Integration under the Orientation of Career Trajectory Portrait

Applied Mathematics and Nonlinear Sciences

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

The fusion of industry and education plays a pivotal role in optimizing talent training structures, facilitating the conversion of scientific and technological...

Exploration of Talent Cultivation Strategies for Higher Vocational Industry-Teaching Integration under the Orientation of Career Trajectory Portrait

Publicado en línea: 22 may 2024

Páginas: -

Recibido: 10 feb 2024

Aceptado: 20 abr 2024

DOI: https://doi.org/10.2478/amns-2024-1207

Palabras clave
Clustering algorithm, Associative bipartite graph, Collaborative filtering algorithm, Industry-teaching integration

© 2024 Junyan Fan, published by Sciendo

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

Exploration of Talent Cultivation Strategies for Higher Vocational Industry-Teaching Integration under the Orientation of Career Trajectory Portrait

Publicado en línea: 22 may 2024

Páginas: -

Recibido: 10 feb 2024

Aceptado: 20 abr 2024

DOI: https://doi.org/10.2478/amns-2024-1207

Palabras claveClustering algorithm, Associative bipartite graph, Collaborative filtering algorithm, Industry-teaching integration

© 2024 Junyan Fan, published by Sciendo

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

Palabras clave
Clustering algorithm, Associative bipartite graph, Collaborative filtering algorithm, Industry-teaching integration