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<p>For colleges and universities, it is imperative to actively respond to the current new teaching mode and to put information technology into English teaching in the new situation. This paper improves the random forest algorithm by using the sparrow search algorithm to calculate the Gini index score of each evaluation index feature. By addressing the impurities of nodes in the tree, the importance of each index in the forest can be ranked. Further, the improved random forest algorithm is combined with convolutional neural network to construct the obtained English teaching evaluation method to explore the practical effect of the informationized English teaching integration path established in this paper in teaching. The results show that the average scores of the five colleges and universities’ average scores of “technology-enabled teaching” and “awareness and attitude” indicators are high, respectively 4.27 and 4.16. In contrast, the scores of “social support” indicators are low. The average score for “social support” is low, only 3.24. After the teaching practice, the number of “excellent” scores in teaching expansion increased by 15. This paper’s integration path for informationized English teaching has precise results, a certain degree of universality, and serves as a reference for future exploration of the path.</p>
</abstract>

For colleges and universities, it is imperative to actively respond to the current new teaching mode and to put information technology into English teaching in the new situation. This paper improves the random forest algorithm by using the sparrow search algorithm to calculate the Gini index score of each evaluation index feature. By addressing the impurities of nodes in the tree, the importance of each index in the forest can be ranked. Further, the improved random forest algorithm is combined with convolutional neural network to construct the obtained English teaching evaluation method to explore the practical effect of the informationized English teaching integration path established in this paper in teaching. The results show that the average scores of the five colleges and universities’ average scores of “technology-enabled teaching” and “awareness and attitude” indicators are high, respectively 4.27 and 4.16. In contrast, the scores of “social support” indicators are low. The average score for “social support” is low, only 3.24. After the teaching practice, the number of “excellent” scores in teaching expansion increased by 15. This paper’s integration path for informationized English teaching has precise results, a certain degree of universality, and serves as a reference for future exploration of the path.

Analysis of Integration Path in English Education and Teaching in Colleges and Universities under the Background of Informatization

Applied Mathematics and Nonlinear Sciences

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

For colleges and universities, it is imperative to actively respond to the current new teaching mode and to put information technology into English teaching in...

Analysis of Integration Path in English Education and Teaching in Colleges and Universities under the Background of Informatization

Data publikacji: 03 maj 2024

Zakres stron: -

Otrzymano: 04 kwi 2024

Przyjęty: 15 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-0955

Słowa kluczowe
Random Forest, Sparrow Search Algorithm, Gini Index, Convolutional Neural Network, English Teaching and Learning

© 2024 Xiao Lu, published by Sciendo

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

Analysis of Integration Path in English Education and Teaching in Colleges and Universities under the Background of Informatization

Data publikacji: 03 maj 2024

Zakres stron: -

Otrzymano: 04 kwi 2024

Przyjęty: 15 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-0955

Słowa kluczoweRandom Forest, Sparrow Search Algorithm, Gini Index, Convolutional Neural Network, English Teaching and Learning

© 2024 Xiao Lu, published by Sciendo

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

Słowa kluczowe
Random Forest, Sparrow Search Algorithm, Gini Index, Convolutional Neural Network, English Teaching and Learning