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<p>Aiming at the current characteristics of English teaching in colleges and universities which are too theoretical, this paper combines the ideas of functional linguistics to innovate the teaching method, focusing on constructing a personalized English teaching resources recommendation system to assist English teaching. Among them, in the teaching resources recommendation module, the collaborative filtering algorithm is improved by calculating the learner trust degree and similarity degree, then describing the learner features and learning resources features respectively, constructing the multi-objective optimization function, and solving the optimal value by using the hybrid differential evolutionary algorithm. The constructed English teaching resources recommendation system is applied to actual teaching. For the answer situation of knowledge difficulty category 5, the highest level students can still maintain the average correct rate of 55.21%. In the comparison experiment of innovation effect demonstration, the experimental group and the control group reached statistical significance in fluency index (P=0.038&lt;0.05) and logic index (P=0.001&lt;0.05). This study can promote the practicability of English teaching and increase the effect of cultivating applied talents.</p>
</abstract>

Aiming at the current characteristics of English teaching in colleges and universities which are too theoretical, this paper combines the ideas of functional linguistics to innovate the teaching method, focusing on constructing a personalized English teaching resources recommendation system to assist English teaching. Among them, in the teaching resources recommendation module, the collaborative filtering algorithm is improved by calculating the learner trust degree and similarity degree, then describing the learner features and learning resources features respectively, constructing the multi-objective optimization function, and solving the optimal value by using the hybrid differential evolutionary algorithm. The constructed English teaching resources recommendation system is applied to actual teaching. For the answer situation of knowledge difficulty category 5, the highest level students can still maintain the average correct rate of 55.21%. In the comparison experiment of innovation effect demonstration, the experimental group and the control group reached statistical significance in fluency index (P=0.038<0.05) and logic index (P=0.001<0.05). This study can promote the practicability of English teaching and increase the effect of cultivating applied talents.

An Innovative Demonstration of Functional Linguistic Ideas for Teaching English in Colleges and Universities Based on Multi-objective Optimization Models

Henan Industry and Trade Vocational College Department of Applied Foreign Language Xinzheng

Applied Mathematics and Nonlinear Sciences

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

Aiming at the current characteristics of English teaching in colleges and universities which are too theoretical, this paper combines the ideas of functional...

An Innovative Demonstration of Functional Linguistic Ideas for Teaching English in Colleges and Universities Based on Multi-objective Optimization Models

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 15. Jan. 2024

Akzeptiert: 24. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0626

Schlüsselwörter
Functional linguistics, Teaching resources recommendation, Multi-objective optimization, Differential evolutionary algorithm, College English teaching

© 2023 Lili Tian et al., published by Sciendo

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

An Innovative Demonstration of Functional Linguistic Ideas for Teaching English in Colleges and Universities Based on Multi-objective Optimization Models

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 15. Jan. 2024

Akzeptiert: 24. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0626

SchlüsselwörterFunctional linguistics, Teaching resources recommendation, Multi-objective optimization, Differential evolutionary algorithm, College English teaching

© 2023 Lili Tian et al., published by Sciendo

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

Schlüsselwörter
Functional linguistics, Teaching resources recommendation, Multi-objective optimization, Differential evolutionary algorithm, College English teaching