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<p>Industry-teaching integration of vocational education is a systematic and innovative schooling system that effectively integrates the resources of the government, vocational colleges and industrial enterprises to realize the coordinated development of applied talent training. This study focuses on using multi-scale feature fusion to study the classroom attention of students in the talent training classroom. It sets up the classroom facial expression detection and head posture estimation modules to realize the intelligent teaching mode reform of teaching, learning and doing integration. Then based on the background of integration of industry and education, combined with the concept of triangular helix, explore the talent training innovation model of higher vocational education, and utilize the QCA method to carry out empirical analysis to explore the synergistic path of integration of industry and education and talent training. The average accuracy of the constructed model for identifying student expressions has been shown to be 93.56%. The original coverage (0.38651) of big business * project motivation * project constraints is the highest, which explains more than 38% of the resultant variables, which is about to be nearly 40% of the industry-teaching fusion projects obtaining high project effectiveness through this path. The synergistic model of industry-teaching integration and talent cultivation in higher vocational education is provided with new ideas by this study.</p>
</abstract>

Industry-teaching integration of vocational education is a systematic and innovative schooling system that effectively integrates the resources of the government, vocational colleges and industrial enterprises to realize the coordinated development of applied talent training. This study focuses on using multi-scale feature fusion to study the classroom attention of students in the talent training classroom. It sets up the classroom facial expression detection and head posture estimation modules to realize the intelligent teaching mode reform of teaching, learning and doing integration. Then based on the background of integration of industry and education, combined with the concept of triangular helix, explore the talent training innovation model of higher vocational education, and utilize the QCA method to carry out empirical analysis to explore the synergistic path of integration of industry and education and talent training. The average accuracy of the constructed model for identifying student expressions has been shown to be 93.56%. The original coverage (0.38651) of big business * project motivation * project constraints is the highest, which explains more than 38% of the resultant variables, which is about to be nearly 40% of the industry-teaching fusion projects obtaining high project effectiveness through this path. The synergistic model of industry-teaching integration and talent cultivation in higher vocational education is provided with new ideas by this study.

Exploration of Talent Cultivation Mode for Industry-Teaching Integration in Higher Vocational Education Based on Multi-scale Feature Integration

Applied Mathematics and Nonlinear Sciences

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

Industry-teaching integration of vocational education is a systematic and innovative schooling system that effectively integrates the resources of the...

Exploration of Talent Cultivation Mode for Industry-Teaching Integration in Higher Vocational Education Based on Multi-scale Feature Integration

Online veröffentlicht: 03. Mai 2024

Seitenbereich: -

Eingereicht: 14. Apr. 2024

Akzeptiert: 27. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-0926

Schlüsselwörter
Multi-scale features, Face expression detection, Head pose estimation, QCA method, Industry-education integration

© 2024 Weilu Liu, published by Sciendo

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

Exploration of Talent Cultivation Mode for Industry-Teaching Integration in Higher Vocational Education Based on Multi-scale Feature Integration

Online veröffentlicht: 03. Mai 2024

Seitenbereich: -

Eingereicht: 14. Apr. 2024

Akzeptiert: 27. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-0926

SchlüsselwörterMulti-scale features, Face expression detection, Head pose estimation, QCA method, Industry-education integration

© 2024 Weilu Liu, published by Sciendo

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

Schlüsselwörter
Multi-scale features, Face expression detection, Head pose estimation, QCA method, Industry-education integration