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

<p>In order to effectively improve college students’ English reading ability, this paper introduces multimodal teaching to analyze the way of teaching English reading in college. This paper firstly dismantles the multimodal teaching model, which is divided into multimodal teaching and the incorporation of the convolutional neural network, and analyzes the current situation of college English reading teaching. Secondly, we select the college English reading teaching modality, construct a multimodal fusion model for college English reading teaching and optimize this model to analyze the convergence and time complexity. Through the analysis of the fusion performance, we found that the accuracy and the pick-up degree of the pre-fusion increased by 22.25% and decreased by 18.22% compared with the unimodal fusion, and the number of participants increased by 61.51 Mb, which was not much different from the middle and late comparison, so the pre-fusion was selected to have the highest practicality. Finally, through the survey statistics on the application of multimodal teaching of college English reading, more than 80% of students and teachers think that multimodal teaching of college English reading can improve their English reading ability and teaching ability. So this teaching method improves their reading ability and enriches the diversity of teaching modes at the same time, which is worth promoting.</p>
</abstract>

In order to effectively improve college students’ English reading ability, this paper introduces multimodal teaching to analyze the way of teaching English reading in college. This paper firstly dismantles the multimodal teaching model, which is divided into multimodal teaching and the incorporation of the convolutional neural network, and analyzes the current situation of college English reading teaching. Secondly, we select the college English reading teaching modality, construct a multimodal fusion model for college English reading teaching and optimize this model to analyze the convergence and time complexity. Through the analysis of the fusion performance, we found that the accuracy and the pick-up degree of the pre-fusion increased by 22.25% and decreased by 18.22% compared with the unimodal fusion, and the number of participants increased by 61.51 Mb, which was not much different from the middle and late comparison, so the pre-fusion was selected to have the highest practicality. Finally, through the survey statistics on the application of multimodal teaching of college English reading, more than 80% of students and teachers think that multimodal teaching of college English reading can improve their English reading ability and teaching ability. So this teaching method improves their reading ability and enriches the diversity of teaching modes at the same time, which is worth promoting.

A Study on College English Reading Teaching for Multimodal Teaching Model

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"A Study on College English Reading Teaching for Multimodal Teaching Model"}

In order to effectively improve college students’ English reading ability, this paper introduces multimodal teaching to analyze the way of teaching English...

A Study on College English Reading Teaching for Multimodal Teaching Model

Publié en ligne: 04 oct. 2023

Pages: -

Reçu: 01 janv. 2023

Accepté: 14 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00474

Mots clés
English reading, Volume neural network, Multimodal fusion, Convergence, Temporal complexity

© 2023 Wei Wang et al., published by Sciendo

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

A Study on College English Reading Teaching for Multimodal Teaching Model

Publié en ligne: 04 oct. 2023

Pages: -

Reçu: 01 janv. 2023

Accepté: 14 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00474

Mots clésEnglish reading, Volume neural network, Multimodal fusion, Convergence, Temporal complexity

© 2023 Wei Wang et al., published by Sciendo

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

Mots clés
English reading, Volume neural network, Multimodal fusion, Convergence, Temporal complexity