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<p>The rapid evolution of technology catalyzes innovation in English teaching methodologies, rendering traditional approaches—predominantly reliant on singular language modalities—insufficient for modern educational demands. This paper introduces a multimodal English teaching model anchored in multimodal discourse analysis and interaction theory to address these evolving needs. Utilizing a dual threshold extraction algorithm for chunked cumulative frame difference, the model effectively identifies semantic key frames during multimodal English instruction. Additionally, it incorporates a composite convolutional neural network that merges spatio-temporal features to delineate the dynamics of the teaching videos, further enhanced by the LK optical flow method. The integration of a pre-interactive LSTM decoder facilitates the fusion of video and textual features, culminating in the construction of an annotated English teaching video model. This model was applied to analyze the classroom discourse of high school sophomores’ English teachers in X city. Analysis revealed that the duration of classroom discourse varied between 14 to 19 minutes per session, with the introductory segment alone accounting for 307.54 seconds—12.81% of the total lesson duration—of purely linguistic content. Furthermore, the average number of questions posed by teachers per lesson was 73.17, indicating a high reliance on verifying discourse strategies, which constituted 92.22% of the discourse. This technological approach to analyzing classroom discourse provides novel insights and valuable references for refining English teaching strategies, demonstrating the effectiveness of integrating advanced computational techniques in educational settings.</p>
</abstract>

The rapid evolution of technology catalyzes innovation in English teaching methodologies, rendering traditional approaches—predominantly reliant on singular language modalities—insufficient for modern educational demands. This paper introduces a multimodal English teaching model anchored in multimodal discourse analysis and interaction theory to address these evolving needs. Utilizing a dual threshold extraction algorithm for chunked cumulative frame difference, the model effectively identifies semantic key frames during multimodal English instruction. Additionally, it incorporates a composite convolutional neural network that merges spatio-temporal features to delineate the dynamics of the teaching videos, further enhanced by the LK optical flow method. The integration of a pre-interactive LSTM decoder facilitates the fusion of video and textual features, culminating in the construction of an annotated English teaching video model. This model was applied to analyze the classroom discourse of high school sophomores’ English teachers in X city. Analysis revealed that the duration of classroom discourse varied between 14 to 19 minutes per session, with the introductory segment alone accounting for 307.54 seconds—12.81% of the total lesson duration—of purely linguistic content. Furthermore, the average number of questions posed by teachers per lesson was 73.17, indicating a high reliance on verifying discourse strategies, which constituted 92.22% of the discourse. This technological approach to analyzing classroom discourse provides novel insights and valuable references for refining English teaching strategies, demonstrating the effectiveness of integrating advanced computational techniques in educational settings.

An Analysis on English Teachers’ Effective Classroom Discourse and Its Interactive Model Innovation with the Assistance of Artificial Intelligence

Applied Mathematics and Nonlinear Sciences

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

The rapid evolution of technology catalyzes innovation in English teaching methodologies, rendering traditional approaches—predominantly reliant on singular...

An Analysis on English Teachers’ Effective Classroom Discourse and Its Interactive Model Innovation with the Assistance of Artificial Intelligence

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 08 mar 2024

Accettato: 24 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1657

Parole chiave
Chunked cumulative frame difference, Semantic keyframes, Composite convolutional network, LK optical flow method, LSTM decoder, English discourse analysis

© 2024 Yongjun Zhong., published by Sciendo

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

An Analysis on English Teachers’ Effective Classroom Discourse and Its Interactive Model Innovation with the Assistance of Artificial Intelligence

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 08 mar 2024

Accettato: 24 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1657

Parole chiaveChunked cumulative frame difference, Semantic keyframes, Composite convolutional network, LK optical flow method, LSTM decoder, English discourse analysis

© 2024 Yongjun Zhong., published by Sciendo

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

Parole chiave
Chunked cumulative frame difference, Semantic keyframes, Composite convolutional network, LK optical flow method, LSTM decoder, English discourse analysis