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<p>In the era of big data, the integration of teaching and information technology has become a new trend in the development of modernized vocational education. Taking the big data majors of vocational college M as an example, this study explores the modernization of vocational education governance system, establishes a vocational education resource recommendation model based on K-Means clustering and FunkSVD algorithm, applies the model to analyze the students’ vocational abilities in terms of user profiles, and verifies the performance stability of the resource recommendation model through continuous iterative training of the model. Comparing the changes in students’ vocational abilities before and after using the resource recommendation model, the effects of the vocational education resource recommendation model are explored. It is finally concluded that the vocational skills of the selected students can be categorized into three types: the first type of students has the most muscular comprehensive strength and the highest proportion, 62.15%. The RMSE value of the resource recommendation model gradually decreases with the increase of data volume and iteration number. The RMSE value decreases by 0.06 when the data volume increases 10 times. The vocational ability of the selected students increases by about 0.05~0.15 after the resource recommendation, which verifies the recommendation effect of the model. The study can be used to improve students’ vocational competence and provide intelligent new ideas for the governance system of vocational education.</p>
</abstract>

In the era of big data, the integration of teaching and information technology has become a new trend in the development of modernized vocational education. Taking the big data majors of vocational college M as an example, this study explores the modernization of vocational education governance system, establishes a vocational education resource recommendation model based on K-Means clustering and FunkSVD algorithm, applies the model to analyze the students’ vocational abilities in terms of user profiles, and verifies the performance stability of the resource recommendation model through continuous iterative training of the model. Comparing the changes in students’ vocational abilities before and after using the resource recommendation model, the effects of the vocational education resource recommendation model are explored. It is finally concluded that the vocational skills of the selected students can be categorized into three types: the first type of students has the most muscular comprehensive strength and the highest proportion, 62.15%. The RMSE value of the resource recommendation model gradually decreases with the increase of data volume and iteration number. The RMSE value decreases by 0.06 when the data volume increases 10 times. The vocational ability of the selected students increases by about 0.05~0.15 after the resource recommendation, which verifies the recommendation effect of the model. The study can be used to improve students’ vocational competence and provide intelligent new ideas for the governance system of vocational education.

Exploration on the practice process of modernization of vocational education governance system based on big data

Applied Mathematics and Nonlinear Sciences

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

In the era of big data, the integration of teaching and information technology has become a new trend in the development of modernized vocational education....

Exploration on the practice process of modernization of vocational education governance system based on big data

Data publikacji: 03 maj 2024

Zakres stron: -

Otrzymano: 13 kwi 2024

Przyjęty: 22 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-0937

Słowa kluczowe
K-Means clustering, FunkSVD, User profiling, Resource recommendation, Vocational education

© 2024 Zhonghua Yu et al., published by Sciendo

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

Exploration on the practice process of modernization of vocational education governance system based on big data

Data publikacji: 03 maj 2024

Zakres stron: -

Otrzymano: 13 kwi 2024

Przyjęty: 22 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-0937

Słowa kluczoweK-Means clustering, FunkSVD, User profiling, Resource recommendation, Vocational education

© 2024 Zhonghua Yu et al., published by Sciendo

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

Słowa kluczowe
K-Means clustering, FunkSVD, User profiling, Resource recommendation, Vocational education