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

<p>With the rapid evolution of the socio-economic landscape and ongoing adjustments in industrial structures, the integration of industry and education has emerged as a pivotal strategy for enhancing vocational education. This approach is crucial for elevating teaching quality and fostering comprehensive student development. In this paper, we utilize cluster analysis to evaluate the integration of industry and education within higher vocational boutique courses. Z school was chosen for the study, where we collected a substantial dataset via questionnaires. We used factor analysis to extract significant influencers of course quality and then applied the K-means clustering algorithm to find the optimal center of mass for the integration. Our analysis distinguishes the disparities among different courses. The study finally classified the course quality into four categories: course A (14.25%), course B (31.25%), course C (33.00%), and course D (21.5). In terms of course quality, the highest-rated course was Course B, with an average score of 90.03, and the lowest-rated course was Course D, with an average score of 70.76, a difference of almost one-quarter. The level of course quality largely influences the results of course evaluation, but the degree of perceived interest and effectiveness are the key factors in improving course quality. The reform and development of higher education are strongly supported and referenced by this study.</p>
</abstract>

With the rapid evolution of the socio-economic landscape and ongoing adjustments in industrial structures, the integration of industry and education has emerged as a pivotal strategy for enhancing vocational education. This approach is crucial for elevating teaching quality and fostering comprehensive student development. In this paper, we utilize cluster analysis to evaluate the integration of industry and education within higher vocational boutique courses. Z school was chosen for the study, where we collected a substantial dataset via questionnaires. We used factor analysis to extract significant influencers of course quality and then applied the K-means clustering algorithm to find the optimal center of mass for the integration. Our analysis distinguishes the disparities among different courses. The study finally classified the course quality into four categories: course A (14.25%), course B (31.25%), course C (33.00%), and course D (21.5). In terms of course quality, the highest-rated course was Course B, with an average score of 90.03, and the lowest-rated course was Course D, with an average score of 70.76, a difference of almost one-quarter. The level of course quality largely influences the results of course evaluation, but the degree of perceived interest and effectiveness are the key factors in improving course quality. The reform and development of higher education are strongly supported and referenced by this study.

Quality Analysis of Industry-Teaching Integration of Higher Vocational Boutique Courses - Based on Cluster Analysis Algorithm

China University of Mining and Technology

Applied Mathematics and Nonlinear Sciences

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

With the rapid evolution of the socio-economic landscape and ongoing adjustments in industrial structures, the integration of industry and education has...

Quality Analysis of Industry-Teaching Integration of Higher Vocational Boutique Courses - Based on Cluster Analysis Algorithm

Online veröffentlicht: 22. Mai 2024

Seitenbereich: -

Eingereicht: 30. Jan. 2024

Akzeptiert: 10. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1194

Schlüsselwörter
Cluster analysis algorithm, Factor analysis, K-means clustering algorithm, Course quality

© 2024 Chen Chen et al., published by Sciendo

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

Quality Analysis of Industry-Teaching Integration of Higher Vocational Boutique Courses - Based on Cluster Analysis Algorithm

Online veröffentlicht: 22. Mai 2024

Seitenbereich: -

Eingereicht: 30. Jan. 2024

Akzeptiert: 10. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1194

SchlüsselwörterCluster analysis algorithm, Factor analysis, K-means clustering algorithm, Course quality

© 2024 Chen Chen et al., published by Sciendo

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

Schlüsselwörter
Cluster analysis algorithm, Factor analysis, K-means clustering algorithm, Course quality