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

<p>Rural revitalization, talent first, from the high-quality farmer’s cultivation policy as well as vocational education characteristics research, to achieve high-quality farmers’ talent cultivation. This paper analyzes the status quo of the cultivation of high-quality farmers, constructs the cultivation model of high-quality farmers through the OBE-PDCA method, and constructs the cultivation effect evaluation model of high-quality farmers based on the BP-PCA method by combining BP neural network and principal component analysis. On this basis, the cultivation path of management-type high-quality farmers is taken as an example to explore the effect of OBE-PDCA in constructing the cultivation model of high-quality farmers as well as the cultivation effect of high-quality farmers. The results show that the averages of the cultivation of provincial higher vocational colleges and universities in the four dimensions of PDCA are 16.07, 10.51, 13.48, and 22.58, and the F-values of the overall test are 2.4, 7.8, 4.5, and 4.1, with the correlation coefficients of 0.46, 0.45, and 0.59, respectively. The resulting difference in the weight scores of Δ ≥ 10 is 1 item, 5 ≤ Δ ＜ 10 is 6 items, 0≤ Δ ＜ 5 is 1 item, with good cultivation effects. Item, there is a good cultivation effect. The method constructed in this paper can effectively enhance the cultivation effect of high-quality farmers and promote rural revitalization.</p>
</abstract>

Rural revitalization, talent first, from the high-quality farmer’s cultivation policy as well as vocational education characteristics research, to achieve high-quality farmers’ talent cultivation. This paper analyzes the status quo of the cultivation of high-quality farmers, constructs the cultivation model of high-quality farmers through the OBE-PDCA method, and constructs the cultivation effect evaluation model of high-quality farmers based on the BP-PCA method by combining BP neural network and principal component analysis. On this basis, the cultivation path of management-type high-quality farmers is taken as an example to explore the effect of OBE-PDCA in constructing the cultivation model of high-quality farmers as well as the cultivation effect of high-quality farmers. The results show that the averages of the cultivation of provincial higher vocational colleges and universities in the four dimensions of PDCA are 16.07, 10.51, 13.48, and 22.58, and the F-values of the overall test are 2.4, 7.8, 4.5, and 4.1, with the correlation coefficients of 0.46, 0.45, and 0.59, respectively. The resulting difference in the weight scores of Δ ≥ 10 is 1 item, 5 ≤ Δ ＜ 10 is 6 items, 0≤ Δ ＜ 5 is 1 item, with good cultivation effects. Item, there is a good cultivation effect. The method constructed in this paper can effectively enhance the cultivation effect of high-quality farmers and promote rural revitalization.

A Study on the Construction of Cultivating High-Quality Farmers in Higher Vocational Colleges and Universities under the OBE-PDCA Approach

Applied Mathematics and Nonlinear Sciences

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

Rural revitalization, talent first, from the high-quality farmer’s cultivation policy as well as vocational education characteristics research, to achieve...

A Study on the Construction of Cultivating High-Quality Farmers in Higher Vocational Colleges and Universities under the OBE-PDCA Approach

Data publikacji: 16 gru 2023

Zakres stron: -

Otrzymano: 07 mar 2023

Przyjęty: 26 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01489

Słowa kluczowe
OBE-PDCA model, BP neural network, Principal component analysis, BP-PCA model, Highly qualified farmers

© 2023 Jing Han et al., published by Sciendo

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

A Study on the Construction of Cultivating High-Quality Farmers in Higher Vocational Colleges and Universities under the OBE-PDCA Approach

Data publikacji: 16 gru 2023

Zakres stron: -

Otrzymano: 07 mar 2023

Przyjęty: 26 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01489

Słowa kluczoweOBE-PDCA model, BP neural network, Principal component analysis, BP-PCA model, Highly qualified farmers

© 2023 Jing Han et al., published by Sciendo

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

Słowa kluczowe
OBE-PDCA model, BP neural network, Principal component analysis, BP-PCA model, Highly qualified farmers