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<p>In this paper, the PSO-LSTM algorithm is used to construct a model for evaluating students’ language proficiency, and the improvement of the acceleration factor and inertia weights is used to adjust the influence of the individual optimal position and the global optimal position in the speed update. For model performance judgment after optimization of LSTM hyperparameters, the PSO fitness function is replaced by MAPE and MSE functions. Finally, the student’s English composition texts were used as experimental parameters and input into the language proficiency evaluation model to verify the model performance and analyze the influence of English language literature on language proficiency. The results show that English language literature is effective in predicting 18.55% of the variance in English proficiency level for self-efficacy, 8.17% of the variance in English proficiency level for language attitude, and 0.47% of the variance in English proficiency level for learning anxiety, which indicates that English language literature has a significant effect on students’ English proficiency. This paper investigates the role mechanism of the English language and literature in the cultivation of student’s English proficiency, which provides a theoretical reference for the reform of English teaching in colleges and universities.</p>
</abstract>

In this paper, the PSO-LSTM algorithm is used to construct a model for evaluating students’ language proficiency, and the improvement of the acceleration factor and inertia weights is used to adjust the influence of the individual optimal position and the global optimal position in the speed update. For model performance judgment after optimization of LSTM hyperparameters, the PSO fitness function is replaced by MAPE and MSE functions. Finally, the student’s English composition texts were used as experimental parameters and input into the language proficiency evaluation model to verify the model performance and analyze the influence of English language literature on language proficiency. The results show that English language literature is effective in predicting 18.55% of the variance in English proficiency level for self-efficacy, 8.17% of the variance in English proficiency level for language attitude, and 0.47% of the variance in English proficiency level for learning anxiety, which indicates that English language literature has a significant effect on students’ English proficiency. This paper investigates the role mechanism of the English language and literature in the cultivation of student’s English proficiency, which provides a theoretical reference for the reform of English teaching in colleges and universities.

The Role of English Language and Literature in the Cultivation of Students’ Language Proficiency in the Context of Deep Learning

Applied Mathematics and Nonlinear Sciences

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

In this paper, the PSO-LSTM algorithm is used to construct a model for evaluating students’ language proficiency, and the improvement of the acceleration...

The Role of English Language and Literature in the Cultivation of Students’ Language Proficiency in the Context of Deep Learning

Data publikacji: 02 gru 2023

Zakres stron: -

Otrzymano: 15 lut 2023

Przyjęty: 12 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01343

Słowa kluczowe
PSO-LSTM algorithm, Speedup factor, Global optimum, English language and literature, Hyperparameter optimization

© 2023 Manli Jia, published by Sciendo

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

The Role of English Language and Literature in the Cultivation of Students’ Language Proficiency in the Context of Deep Learning

Data publikacji: 02 gru 2023

Zakres stron: -

Otrzymano: 15 lut 2023

Przyjęty: 12 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01343

Słowa kluczowePSO-LSTM algorithm, Speedup factor, Global optimum, English language and literature, Hyperparameter optimization

© 2023 Manli Jia, published by Sciendo

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

Słowa kluczowe
PSO-LSTM algorithm, Speedup factor, Global optimum, English language and literature, Hyperparameter optimization