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

<p>This paper follows the guiding principles of eco-linguistics for English teaching, improves the encoder of the question generation model, uses the gated self-attention mechanism to refine the textual information, obtains the embedded representations of the articles, fuses them with the embedded representations of the answers, and constructs a new automatic generation model of the English teaching exercises as a way of assisting in the digital transformation of the English classroom. After the controlled test, it was found that the post-test control class scored 71.954 points, and the experimental class scored 84.309 points, which was 12.355 points higher than the control class, with Sig. The two-tailed test resulted in a coefficient of 0.035, which was less than 0.05, and there was a significant difference in the post-test scores. In the survey of interest in learning, the average number of respondents who chose “strongly agree” or “agree” was 89%, 8.5% said “uncertain”, and 2.5% “disagreed”. Overall, the interest of the experimental class students in English learning has improved significantly. They not only pay more attention in class but also have a higher interest in practicing English after class. The digital transformation of traditional English classroom teaching is addressed in this study through the provision of new ideas and methods.</p>
</abstract>

This paper follows the guiding principles of eco-linguistics for English teaching, improves the encoder of the question generation model, uses the gated self-attention mechanism to refine the textual information, obtains the embedded representations of the articles, fuses them with the embedded representations of the answers, and constructs a new automatic generation model of the English teaching exercises as a way of assisting in the digital transformation of the English classroom. After the controlled test, it was found that the post-test control class scored 71.954 points, and the experimental class scored 84.309 points, which was 12.355 points higher than the control class, with Sig. The two-tailed test resulted in a coefficient of 0.035, which was less than 0.05, and there was a significant difference in the post-test scores. In the survey of interest in learning, the average number of respondents who chose “strongly agree” or “agree” was 89%, 8.5% said “uncertain”, and 2.5% “disagreed”. Overall, the interest of the experimental class students in English learning has improved significantly. They not only pay more attention in class but also have a higher interest in practicing English after class. The digital transformation of traditional English classroom teaching is addressed in this study through the provision of new ideas and methods.

Digital Transformation Paths in English Language Teaching from an Eco-Linguistic Perspective

Applied Mathematics and Nonlinear Sciences

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

This paper follows the guiding principles of eco-linguistics for English teaching, improves the encoder of the question generation model, uses the gated...

Digital Transformation Paths in English Language Teaching from an Eco-Linguistic Perspective

Online veröffentlicht: 05. Juli 2024

Seitenbereich: -

Eingereicht: 25. März 2024

Akzeptiert: 09. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1647

Schlüsselwörter
Ecolinguistics, Natural language processing, Graph attention networks, English language teaching

© 2024 Zhenzhen Tang, published by Sciendo

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

Digital Transformation Paths in English Language Teaching from an Eco-Linguistic Perspective

Online veröffentlicht: 05. Juli 2024

Seitenbereich: -

Eingereicht: 25. März 2024

Akzeptiert: 09. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1647

SchlüsselwörterEcolinguistics, Natural language processing, Graph attention networks, English language teaching

© 2024 Zhenzhen Tang, published by Sciendo

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

Schlüsselwörter
Ecolinguistics, Natural language processing, Graph attention networks, English language teaching