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

<p>This study investigates effective integration of node and edge features through N-way K-shot problem definition and iterative updating of graph structure information. The flexibility and effectiveness of the model are enhanced by using the gate function of the adaptive layer to control the degree of neighborhood aggregation and optimize the edge features through the double stochastic normalization technique. The introduction of the LGACN model strengthens the clustering performance through the Attention Network, and improves the adaptability and accuracy of the teaching model. The empirical Analysis shows that compared with the traditional method, the model has outstanding performance in enhancing students’ knowledge understanding, skill application and vocational quality, especially the student satisfaction in practical teaching effect and student-student mutual evaluation is significantly improved. Among the 256 students in the experimental class, the comprehensive satisfaction score increased from 68.15-80.21 to 80.21-89.89, significantly improving teaching effectiveness. By deeply optimizing the practical teaching mode of college English, this study provides new perspectives and effective strategies for language teaching in online learning environments, which helps to improve teaching effectiveness and student satisfaction.</p>
</abstract>

This study investigates effective integration of node and edge features through N-way K-shot problem definition and iterative updating of graph structure information. The flexibility and effectiveness of the model are enhanced by using the gate function of the adaptive layer to control the degree of neighborhood aggregation and optimize the edge features through the double stochastic normalization technique. The introduction of the LGACN model strengthens the clustering performance through the Attention Network, and improves the adaptability and accuracy of the teaching model. The empirical Analysis shows that compared with the traditional method, the model has outstanding performance in enhancing students’ knowledge understanding, skill application and vocational quality, especially the student satisfaction in practical teaching effect and student-student mutual evaluation is significantly improved. Among the 256 students in the experimental class, the comprehensive satisfaction score increased from 68.15-80.21 to 80.21-89.89, significantly improving teaching effectiveness. By deeply optimizing the practical teaching mode of college English, this study provides new perspectives and effective strategies for language teaching in online learning environments, which helps to improve teaching effectiveness and student satisfaction.

Research on Optimization of University English Practice Teaching Mode Based on Graph Structure in Online Learning Environment

Applied Mathematics and Nonlinear Sciences

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

This study investigates effective integration of node and edge features through N-way K-shot problem definition and iterative updating of graph structure...

Research on Optimization of University English Practice Teaching Mode Based on Graph Structure in Online Learning Environment

Publié en ligne: 26 févr. 2024

Pages: -

Reçu: 02 janv. 2024

Accepté: 07 janv. 2024

DOI: https://doi.org/10.2478/amns-2024-0347

Mots clés
Graph structure, Neighborhood aggregation, Normalization, LGACN, College English

© 2024 Linyan Wang et al., published by Sciendo

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

Research on Optimization of University English Practice Teaching Mode Based on Graph Structure in Online Learning Environment

Publié en ligne: 26 févr. 2024

Pages: -

Reçu: 02 janv. 2024

Accepté: 07 janv. 2024

DOI: https://doi.org/10.2478/amns-2024-0347

Mots clésGraph structure, Neighborhood aggregation, Normalization, LGACN, College English

© 2024 Linyan Wang et al., published by Sciendo

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

Mots clés
Graph structure, Neighborhood aggregation, Normalization, LGACN, College English