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<p>In this paper, we first constructed an online interactive English teaching model through the interaction hierarchy tower model and adopted a data-driven social cognitive network analysis approach to explore the evolutionary dynamics of cross-border e-commerce English learning communities in higher education and their potential correlation with the development of participants’ interaction patterns. Then, the online synchronous and asynchronous interaction patterns of learners are compared and integrated by extracting the characteristics of online interactive teaching learners. Finally, an instructional design for an online English for Cross-border E-commerce course was used to explore the differences and associations between online interactive teaching modes. The results show that in the teaching experiment, students have 39 nodes in the synchronous interaction network (SMN) and 34 nodes in the asynchronous interaction network (UMN), and the degree centrality and eigenvector centrality Rank=4.07 and 3.15 of the SMN are also significantly higher than those of the three interaction networks. The SMN interaction model is found to be the most efficient in the online interaction teaching process. This study helps to improve the dynamic regulation and precise guidance of the online learning process for cross-border e-commerce English, which ultimately enhances the quality of online teaching.</p>
</abstract>

In this paper, we first constructed an online interactive English teaching model through the interaction hierarchy tower model and adopted a data-driven social cognitive network analysis approach to explore the evolutionary dynamics of cross-border e-commerce English learning communities in higher education and their potential correlation with the development of participants’ interaction patterns. Then, the online synchronous and asynchronous interaction patterns of learners are compared and integrated by extracting the characteristics of online interactive teaching learners. Finally, an instructional design for an online English for Cross-border E-commerce course was used to explore the differences and associations between online interactive teaching modes. The results show that in the teaching experiment, students have 39 nodes in the synchronous interaction network (SMN) and 34 nodes in the asynchronous interaction network (UMN), and the degree centrality and eigenvector centrality Rank=4.07 and 3.15 of the SMN are also significantly higher than those of the three interaction networks. The SMN interaction model is found to be the most efficient in the online interaction teaching process. This study helps to improve the dynamic regulation and precise guidance of the online learning process for cross-border e-commerce English, which ultimately enhances the quality of online teaching.

The Construction of Online Interactive Teaching Mode of Cross-border E-commerce English in Colleges and Universities under the Background of Internet

Applied Mathematics and Nonlinear Sciences

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

In this paper, we first constructed an online interactive English teaching model through the interaction hierarchy tower model and adopted a data-driven social...

The Construction of Online Interactive Teaching Mode of Cross-border E-commerce English in Colleges and Universities under the Background of Internet

Published Online: Dec 18, 2023

Page range: -

Received: Jan 03, 2023

Accepted: Jun 28, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01515

Keywords
Interaction hierarchy towers, Social cognitive networks, Data-driven, Community evolution, Feature extraction

© 2023 Qian Zhang et al., published by Sciendo

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

The Construction of Online Interactive Teaching Mode of Cross-border E-commerce English in Colleges and Universities under the Background of Internet

Published Online: Dec 18, 2023

Page range: -

Received: Jan 03, 2023

Accepted: Jun 28, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01515

KeywordsInteraction hierarchy towers, Social cognitive networks, Data-driven, Community evolution, Feature extraction

© 2023 Qian Zhang et al., published by Sciendo

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

Keywords
Interaction hierarchy towers, Social cognitive networks, Data-driven, Community evolution, Feature extraction