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<p>Under the new round of scientific and technological revolution and industrial change, the integration of vocational education and industry is an important direction of educational development. In this paper, the structure of the learning path network of higher vocational professional courses is analyzed, and through the analysis of the elements of learning behaviors, the learning behaviors are mined by adopting sequence relations, and the behavioral structure model is established. The assessment of the learning path under the integration of industry and education courses was finished using uncertainty association coefficients and time association coefficients. After building the learning path network for vocational education, the subjective characteristics of students under vocational education were detected using isolated point analysis. The detection results show that the correlation coefficient between the first pair of typical variables is 0.488. The Wilks statistical test results for typical correlation coefficients show that the p-value of the first typical correlation coefficient is 0.098, which is less than 10%. The correlation study of the environment of integration of education and industry and the effect of integration can be simplified to study only the relationship between the first pair of typical variables. By analyzing the main characteristics, the main body for vocational education and industry integration is clarified, which creates a digital-driven impetus for the integration of industry and education.</p>
</abstract>

Under the new round of scientific and technological revolution and industrial change, the integration of vocational education and industry is an important direction of educational development. In this paper, the structure of the learning path network of higher vocational professional courses is analyzed, and through the analysis of the elements of learning behaviors, the learning behaviors are mined by adopting sequence relations, and the behavioral structure model is established. The assessment of the learning path under the integration of industry and education courses was finished using uncertainty association coefficients and time association coefficients. After building the learning path network for vocational education, the subjective characteristics of students under vocational education were detected using isolated point analysis. The detection results show that the correlation coefficient between the first pair of typical variables is 0.488. The Wilks statistical test results for typical correlation coefficients show that the p-value of the first typical correlation coefficient is 0.098, which is less than 10%. The correlation study of the environment of integration of education and industry and the effect of integration can be simplified to study only the relationship between the first pair of typical variables. By analyzing the main characteristics, the main body for vocational education and industry integration is clarified, which creates a digital-driven impetus for the integration of industry and education.

Vocational Education and Industry Integration and Symbiosis of Subjects and Digital Drivers

Applied Mathematics and Nonlinear Sciences

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

Under the new round of scientific and technological revolution and industrial change, the integration of vocational education and industry is an important...

Vocational Education and Industry Integration and Symbiosis of Subjects and Digital Drivers

Online veröffentlicht: 13. Dez. 2023

Seitenbereich: -

Eingereicht: 14. März 2023

Akzeptiert: 23. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01456

Schlüsselwörter
Behavioral elements, Sequential relationship, Behavioral structural model, Isolated point analysis, Time correlation coefficient

© 2023 Yi Guo, published by Sciendo

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

Vocational Education and Industry Integration and Symbiosis of Subjects and Digital Drivers

Online veröffentlicht: 13. Dez. 2023

Seitenbereich: -

Eingereicht: 14. März 2023

Akzeptiert: 23. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01456

SchlüsselwörterBehavioral elements, Sequential relationship, Behavioral structural model, Isolated point analysis, Time correlation coefficient

© 2023 Yi Guo, published by Sciendo

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

Schlüsselwörter
Behavioral elements, Sequential relationship, Behavioral structural model, Isolated point analysis, Time correlation coefficient