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

<p>This paper first proposes English learning software based on personalized recommendations to enhance students’ interest and motivation in learning college English based on artificial intelligence teaching. Secondly, it consists of constructing a learner model, an English learning resource personalized recommendation algorithm to constitute an English learning resource recommendation model, and using a collaborative decomposition algorithm and a regularization algorithm to integrate the potential, multifaceted trust relationship between users with the traditional scoring matrix, a process that emphasizes that trust between users in English teaching varies by different factors and strengths. Then, a personalized recommendation-based university English learning software was designed, and English learning software applications were analyzed by example. The results showed that the adjusted means of pre and post-test scores of the control group, experimental group one, and experimental group two were 77.37, 80.94, and 75.12, respectively, and the statistical score result F=5.33, P&lt;05 indicated that there was a significant difference in the change of English performance of these three groups, which verified that the teaching software had a facilitating effect on the improvement of English teaching quality.</p>
</abstract>

This paper first proposes English learning software based on personalized recommendations to enhance students’ interest and motivation in learning college English based on artificial intelligence teaching. Secondly, it consists of constructing a learner model, an English learning resource personalized recommendation algorithm to constitute an English learning resource recommendation model, and using a collaborative decomposition algorithm and a regularization algorithm to integrate the potential, multifaceted trust relationship between users with the traditional scoring matrix, a process that emphasizes that trust between users in English teaching varies by different factors and strengths. Then, a personalized recommendation-based university English learning software was designed, and English learning software applications were analyzed by example. The results showed that the adjusted means of pre and post-test scores of the control group, experimental group one, and experimental group two were 77.37, 80.94, and 75.12, respectively, and the statistical score result F=5.33, P<05 indicated that there was a significant difference in the change of English performance of these three groups, which verified that the teaching software had a facilitating effect on the improvement of English teaching quality.

The Application of Artificial Intelligence Teaching Software in College English Teaching

Chengdu University of Information Technology

Applied Mathematics and Nonlinear Sciences

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

This paper first proposes English learning software based on personalized recommendations to enhance students’ interest and motivation in learning college...

The Application of Artificial Intelligence Teaching Software in College English Teaching

Publié en ligne: 15 oct. 2023

Pages: -

Reçu: 23 déc. 2022

Accepté: 25 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00657

Mots clés
Collaborative decomposition algorithm, Regularization algorithm, Personalized recommendation, College English, Teaching software

© 2023 Wenpu Wang et al., published by Sciendo

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

The Application of Artificial Intelligence Teaching Software in College English Teaching

Publié en ligne: 15 oct. 2023

Pages: -

Reçu: 23 déc. 2022

Accepté: 25 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00657

Mots clésCollaborative decomposition algorithm, Regularization algorithm, Personalized recommendation, College English, Teaching software

© 2023 Wenpu Wang et al., published by Sciendo

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

Mots clés
Collaborative decomposition algorithm, Regularization algorithm, Personalized recommendation, College English, Teaching software