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<p>With the progress of the information age, the evaluation of students in higher vocational colleges and universities is not single, and more and more colleges and universities pay more attention to the overall development of students and use diversified evaluation of students’ abilities. This paper constructs a multivariate evaluation model for English teaching in higher vocational school-enterprise cooperation based on deep learning networks and forest stochastic algorithms. By analyzing the application of convolutional neural networks in teaching quality evaluation, Sigmoid and Tanh functions are used to improve the efficiency of English teaching quality evaluation. The bagging method in integrated learning involves averaging the model prediction results of each subset to arrive at the final evaluation results. The multivariate evaluation model is utilized in English teaching evaluation to examine the impact of English teaching under multivariate evaluation. The results show that the evaluation accuracy of the pluralistic evaluation model for English teaching ideas, teaching objectives, teaching contents and teaching effects are 0.981, 0.893, 0.904, and 0.924, respectively, and the students’ liking degree of English is increased from 0.459 to 0.793 under the pluralistic evaluation, which is conducive to the improvement of the teaching effect of English in higher vocational colleges and universities, and provides a new reference for the teaching of English. Perspective.</p>
</abstract>

With the progress of the information age, the evaluation of students in higher vocational colleges and universities is not single, and more and more colleges and universities pay more attention to the overall development of students and use diversified evaluation of students’ abilities. This paper constructs a multivariate evaluation model for English teaching in higher vocational school-enterprise cooperation based on deep learning networks and forest stochastic algorithms. By analyzing the application of convolutional neural networks in teaching quality evaluation, Sigmoid and Tanh functions are used to improve the efficiency of English teaching quality evaluation. The bagging method in integrated learning involves averaging the model prediction results of each subset to arrive at the final evaluation results. The multivariate evaluation model is utilized in English teaching evaluation to examine the impact of English teaching under multivariate evaluation. The results show that the evaluation accuracy of the pluralistic evaluation model for English teaching ideas, teaching objectives, teaching contents and teaching effects are 0.981, 0.893, 0.904, and 0.924, respectively, and the students’ liking degree of English is increased from 0.459 to 0.793 under the pluralistic evaluation, which is conducive to the improvement of the teaching effect of English in higher vocational colleges and universities, and provides a new reference for the teaching of English. Perspective.

Research on Multiple Evaluation of English Teaching in Higher Vocational School-Enterprise Cooperation in the Era of Multimedia Informatization

Applied Mathematics and Nonlinear Sciences

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

With the progress of the information age, the evaluation of students in higher vocational colleges and universities is not single, and more and more colleges...

Research on Multiple Evaluation of English Teaching in Higher Vocational School-Enterprise Cooperation in the Era of Multimedia Informatization

Data publikacji: 16 gru 2023

Zakres stron: -

Otrzymano: 23 mar 2023

Przyjęty: 26 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01488

Słowa kluczowe
Convolutional neural network, Random forest, Sigmoid function, Bagging method, Teaching multivariate evaluation

© 2023 Pei Li, published by Sciendo

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

Research on Multiple Evaluation of English Teaching in Higher Vocational School-Enterprise Cooperation in the Era of Multimedia Informatization

Data publikacji: 16 gru 2023

Zakres stron: -

Otrzymano: 23 mar 2023

Przyjęty: 26 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01488

Słowa kluczoweConvolutional neural network, Random forest, Sigmoid function, Bagging method, Teaching multivariate evaluation

© 2023 Pei Li, published by Sciendo

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

Słowa kluczowe
Convolutional neural network, Random forest, Sigmoid function, Bagging method, Teaching multivariate evaluation