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

<p>In order to meet the changing educational needs, this paper collects the factors related to the teaching mode through the method of multilevel fuzzy evaluation and establishes a multilevel fuzzy evaluation system including the factors of teaching methods, textbook selection, learning resources and so on. To obtain quantitative satisfaction results, fuzzy numbers are employed to calculate the satisfaction evaluation of teaching programs. Establish a judgment matrix using triangular fuzzy numbers and hierarchical analysis to assign weights to different indicators. The evaluation results of each factor are synthesized to obtain the evaluation results for the timeliness of the English teaching method. The results show that although there is not much difference in the basic level of English between the experimental class and the control class, the experimental class, after using the method of this paper, has a measure of qualitative data higher than the standardized metric of 71.195, and the academic grade reaches a full score of 10. It is further verified that by using the method based on multilevel fuzzy evaluation, universities can optimize their teaching mode in a more targeted way to adapt to the increasingly changing educational environment.</p>
</abstract>

In order to meet the changing educational needs, this paper collects the factors related to the teaching mode through the method of multilevel fuzzy evaluation and establishes a multilevel fuzzy evaluation system including the factors of teaching methods, textbook selection, learning resources and so on. To obtain quantitative satisfaction results, fuzzy numbers are employed to calculate the satisfaction evaluation of teaching programs. Establish a judgment matrix using triangular fuzzy numbers and hierarchical analysis to assign weights to different indicators. The evaluation results of each factor are synthesized to obtain the evaluation results for the timeliness of the English teaching method. The results show that although there is not much difference in the basic level of English between the experimental class and the control class, the experimental class, after using the method of this paper, has a measure of qualitative data higher than the standardized metric of 71.195, and the academic grade reaches a full score of 10. It is further verified that by using the method based on multilevel fuzzy evaluation, universities can optimize their teaching mode in a more targeted way to adapt to the increasingly changing educational environment.

Improving the timeliness of college English teaching model based on multilevel fuzzy evaluation

Applied Mathematics and Nonlinear Sciences

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

In order to meet the changing educational needs, this paper collects the factors related to the teaching mode through the method of multilevel fuzzy evaluation...

Improving the timeliness of college English teaching model based on multilevel fuzzy evaluation

Publicado en línea: 11 dic 2023

Páginas: -

Recibido: 29 ene 2023

Aceptado: 21 jun 2023

DOI: https://doi.org/10.2478/amns.2023.2.01438

Palabras clave
Multilevel fuzzy evaluation, Teaching mode, Fuzzy number calculation, Hierarchical analysis, Judgment matrix

© 2023 Bingliang Pang et al., published by Sciendo

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

Improving the timeliness of college English teaching model based on multilevel fuzzy evaluation

Publicado en línea: 11 dic 2023

Páginas: -

Recibido: 29 ene 2023

Aceptado: 21 jun 2023

DOI: https://doi.org/10.2478/amns.2023.2.01438

Palabras claveMultilevel fuzzy evaluation, Teaching mode, Fuzzy number calculation, Hierarchical analysis, Judgment matrix

© 2023 Bingliang Pang et al., published by Sciendo

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

Palabras clave
Multilevel fuzzy evaluation, Teaching mode, Fuzzy number calculation, Hierarchical analysis, Judgment matrix