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<p>This paper first deconstructs the core of the college English precision education model, combines relevant theoretical knowledge, and constructs a college English precision education model based on artificial intelligence. Secondly, the convolutional neural network technology is integrated into the joint probability matrix decomposition model, and the resource recommendation list is generated through the cognitive ability level of students and their individualized needs so as to realize the precise, intelligent recommendation of English education resources. Finally, the performance of the recommendation for teaching English educational resources and the practical effect of the educational model are explored through comparative experiments. The results show that the precision rate and recall rate of the recommendation with a test set of 10% are 0.871 and 0.866, respectively, and the F1 value is 0.88. After the teaching practice, students’ listening ability improved by 0.132, expression ability improved by 0.13, reading ability improved by 0.132, writing ability improved by 0.130, and translation ability improved by 0.128. Based on the paper, the educational model has a positive impact on improving the level of English education in colleges and universities.</p>
</abstract>

This paper first deconstructs the core of the college English precision education model, combines relevant theoretical knowledge, and constructs a college English precision education model based on artificial intelligence. Secondly, the convolutional neural network technology is integrated into the joint probability matrix decomposition model, and the resource recommendation list is generated through the cognitive ability level of students and their individualized needs so as to realize the precise, intelligent recommendation of English education resources. Finally, the performance of the recommendation for teaching English educational resources and the practical effect of the educational model are explored through comparative experiments. The results show that the precision rate and recall rate of the recommendation with a test set of 10% are 0.871 and 0.866, respectively, and the F1 value is 0.88. After the teaching practice, students’ listening ability improved by 0.132, expression ability improved by 0.13, reading ability improved by 0.132, writing ability improved by 0.130, and translation ability improved by 0.128. Based on the paper, the educational model has a positive impact on improving the level of English education in colleges and universities.

The Construction of English Precision Education Model in Colleges and Universities under the Background of Artificial Intelligence

Applied Mathematics and Nonlinear Sciences

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

This paper first deconstructs the core of the college English precision education model, combines relevant theoretical knowledge, and constructs a college...

The Construction of English Precision Education Model in Colleges and Universities under the Background of Artificial Intelligence

Published Online: Nov 27, 2023

Page range: -

Received: Mar 03, 2023

Accepted: Jun 07, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01275

Keywords
Artificial intelligence, Convolutional neural network, Probabilistic matrix decomposition, Intelligent recommendation, Precision education

© 2023 Yu Luo, published by Sciendo

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

The Construction of English Precision Education Model in Colleges and Universities under the Background of Artificial Intelligence

Published Online: Nov 27, 2023

Page range: -

Received: Mar 03, 2023

Accepted: Jun 07, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01275

KeywordsArtificial intelligence, Convolutional neural network, Probabilistic matrix decomposition, Intelligent recommendation, Precision education

© 2023 Yu Luo, published by Sciendo

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

Keywords
Artificial intelligence, Convolutional neural network, Probabilistic matrix decomposition, Intelligent recommendation, Precision education