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<p>In order to improve the status quo of English teaching in colleges and universities, this paper combines the basic concepts of teaching resource base construction with artificial intelligence technology to build a teaching resource base for English in colleges and universities. The overall framework is designed first, followed by the intelligent identity recognition function that is based on artificial intelligence technology. Secondly, on the basis of the text classification algorithm, it designs an intelligent classification retrieval model for English teaching by improving the resource text feature selection algorithm and the resource text representation algorithm. Finally, the performance of the designed college English teaching resource library was tested and explored practically. The results show that the overall teaching resource text feature classification performance has an accuracy, recall, and F1 score of around 0.9. After the teaching practice, the teacher’s lesson preparation and professional competence have been improved by 0.22 and 0.23, respectively, and the student’s English communication and reading competence have been improved by 0.22 and 0.21, respectively. The construction of the English teaching resource base based on this research can improve English teaching.</p>
</abstract>

In order to improve the status quo of English teaching in colleges and universities, this paper combines the basic concepts of teaching resource base construction with artificial intelligence technology to build a teaching resource base for English in colleges and universities. The overall framework is designed first, followed by the intelligent identity recognition function that is based on artificial intelligence technology. Secondly, on the basis of the text classification algorithm, it designs an intelligent classification retrieval model for English teaching by improving the resource text feature selection algorithm and the resource text representation algorithm. Finally, the performance of the designed college English teaching resource library was tested and explored practically. The results show that the overall teaching resource text feature classification performance has an accuracy, recall, and F1 score of around 0.9. After the teaching practice, the teacher’s lesson preparation and professional competence have been improved by 0.22 and 0.23, respectively, and the student’s English communication and reading competence have been improved by 0.22 and 0.21, respectively. The construction of the English teaching resource base based on this research can improve English teaching.

Research on the Construction of English Teaching Resource Base in Colleges and Universities under the Background of Artificial Intelligence

Applied Mathematics and Nonlinear Sciences

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

In order to improve the status quo of English teaching in colleges and universities, this paper combines the basic concepts of teaching resource base...

Research on the Construction of English Teaching Resource Base in Colleges and Universities under the Background of Artificial Intelligence

Data publikacji: 27 lis 2023

Zakres stron: -

Otrzymano: 24 lut 2023

Przyjęty: 07 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01272

Słowa kluczowe
Artificial intelligence, Text feature selection, Text representation algorithms, Teaching resource classification, Teaching resource repository

© 2023 Xinqun Huang et al., published by Sciendo

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

Research on the Construction of English Teaching Resource Base in Colleges and Universities under the Background of Artificial Intelligence

Data publikacji: 27 lis 2023

Zakres stron: -

Otrzymano: 24 lut 2023

Przyjęty: 07 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01272

Słowa kluczoweArtificial intelligence, Text feature selection, Text representation algorithms, Teaching resource classification, Teaching resource repository

© 2023 Xinqun Huang et al., published by Sciendo

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

Słowa kluczowe
Artificial intelligence, Text feature selection, Text representation algorithms, Teaching resource classification, Teaching resource repository