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<p>In order to promote the efficient development of English teaching in Mongolian colleges and universities, this paper constructs an English-personalized teaching system based on trilingual background. Firstly, BP neural network algorithm is used to manage and classify students’ learning data information and detect system teaching evaluation indexes by identifying students’ learning behavior characteristics. Secondly, the K-mean clustering algorithm is invoked in the detection process to improve the detection efficiency to ensure the accuracy of obtaining teaching evaluation indexes. Finally, to verify the effectiveness of the teaching system, a practical analysis of its teaching effect is conducted. The analysis results show that the completion rates of the teaching system constructed in this paper are 78.7% and 86% in course learning and course testing, respectively, and 81% and 75% in information management and knowledge management, respectively. And in the experimental group, the grade passing rate was generally improved by about 42.3%. It proves that the personalized English teaching system constructed in this paper effectively improves the teaching quality of Mongolian college students’ English and is beneficial to the long-term development of English teaching in Mongolian colleges and universities.</p>
</abstract>

In order to promote the efficient development of English teaching in Mongolian colleges and universities, this paper constructs an English-personalized teaching system based on trilingual background. Firstly, BP neural network algorithm is used to manage and classify students’ learning data information and detect system teaching evaluation indexes by identifying students’ learning behavior characteristics. Secondly, the K-mean clustering algorithm is invoked in the detection process to improve the detection efficiency to ensure the accuracy of obtaining teaching evaluation indexes. Finally, to verify the effectiveness of the teaching system, a practical analysis of its teaching effect is conducted. The analysis results show that the completion rates of the teaching system constructed in this paper are 78.7% and 86% in course learning and course testing, respectively, and 81% and 75% in information management and knowledge management, respectively. And in the experimental group, the grade passing rate was generally improved by about 42.3%. It proves that the personalized English teaching system constructed in this paper effectively improves the teaching quality of Mongolian college students’ English and is beneficial to the long-term development of English teaching in Mongolian colleges and universities.

Analysis of the Design and Effectiveness Evaluation of Personalized English Teaching System for Mongolian College Students in Trilingual Context

Applied Mathematics and Nonlinear Sciences

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

In order to promote the efficient development of English teaching in Mongolian colleges and universities, this paper constructs an English-personalized...

Analysis of the Design and Effectiveness Evaluation of Personalized English Teaching System for Mongolian College Students in Trilingual Context

Data publikacji: 07 paź 2023

Zakres stron: -

Otrzymano: 11 gru 2022

Przyjęty: 17 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00519

Słowa kluczowe
English teaching system, BP neural network, Behavioral feature detection, K-mean clustering

© 2023 Xiaohua Zhang, published by Sciendo

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

Analysis of the Design and Effectiveness Evaluation of Personalized English Teaching System for Mongolian College Students in Trilingual Context

Data publikacji: 07 paź 2023

Zakres stron: -

Otrzymano: 11 gru 2022

Przyjęty: 17 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00519

Słowa kluczoweEnglish teaching system, BP neural network, Behavioral feature detection, K-mean clustering

© 2023 Xiaohua Zhang, published by Sciendo

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

Słowa kluczowe
English teaching system, BP neural network, Behavioral feature detection, K-mean clustering