<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In recent years, an increasing number of English teachers have recognized the importance of reading and writing skills. Traditional English teaching methods often overlook the essential link between these skills, which hinders the improvement of students’ overall English proficiency. This paper introduces an adaptive Huber growth curve model to represent cognitive abilities in English reading. It develops an English reading ability detection model using a function-based approach. The model involves word feature extraction, fine-grained learning, and establishing information channels. These processes collectively contribute to a sequence annotation model that not only identifies but also automatically corrects errors in English writing samples. This facilitates integrated intelligent teaching of reading and writing skills. Analysis of English reading and writing capabilities among students at various colleges and universities reveals that the most notable improvement is in students’ critical thinking abilities during English reading, with most scores ranging between 3.5 and 4 and a total of 52 students participating. The difference in writing scores of the subject classes was 1.4186, of which the T value was 3.7855, the DF value was 112.3, and the P value was 0.000, which was significant, and the magnitude of the English writing scores of the subject class A was greater than that of the class B, which indicated that the integration of literacy and writing effectively improved the overall writing skills of the students.</p>
</abstract>

In recent years, an increasing number of English teachers have recognized the importance of reading and writing skills. Traditional English teaching methods often overlook the essential link between these skills, which hinders the improvement of students’ overall English proficiency. This paper introduces an adaptive Huber growth curve model to represent cognitive abilities in English reading. It develops an English reading ability detection model using a function-based approach. The model involves word feature extraction, fine-grained learning, and establishing information channels. These processes collectively contribute to a sequence annotation model that not only identifies but also automatically corrects errors in English writing samples. This facilitates integrated intelligent teaching of reading and writing skills. Analysis of English reading and writing capabilities among students at various colleges and universities reveals that the most notable improvement is in students’ critical thinking abilities during English reading, with most scores ranging between 3.5 and 4 and a total of 52 students participating. The difference in writing scores of the subject classes was 1.4186, of which the T value was 3.7855, the DF value was 112.3, and the P value was 0.000, which was significant, and the magnitude of the English writing scores of the subject class A was greater than that of the class B, which indicated that the integration of literacy and writing effectively improved the overall writing skills of the students.

Analysis of Diversification of Intelligent Teaching in English Literacy Integrated Classroom Empowered by Artificial Intelligence Technology in Colleges and Universities

Department of Humanities and Social Sciences

Applied Mathematics and Nonlinear Sciences

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

In recent years, an increasing number of English teachers have recognized the importance of reading and writing skills. Traditional English teaching methods...

Analysis of Diversification of Intelligent Teaching in English Literacy Integrated Classroom Empowered by Artificial Intelligence Technology in Colleges and Universities

Online veröffentlicht: 22. Mai 2024

Seitenbereich: -

Eingereicht: 01. Feb. 2024

Akzeptiert: 12. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1183

Schlüsselwörter
Adaptive Huter, Feature extraction, Fine-grained learning, Sequence labeling model, English literacy integration

© 2024 Chunhui Yuan et al., published by Sciendo

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

Analysis of Diversification of Intelligent Teaching in English Literacy Integrated Classroom Empowered by Artificial Intelligence Technology in Colleges and Universities

Online veröffentlicht: 22. Mai 2024

Seitenbereich: -

Eingereicht: 01. Feb. 2024

Akzeptiert: 12. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1183

SchlüsselwörterAdaptive Huter, Feature extraction, Fine-grained learning, Sequence labeling model, English literacy integration

© 2024 Chunhui Yuan et al., published by Sciendo

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

Schlüsselwörter
Adaptive Huter, Feature extraction, Fine-grained learning, Sequence labeling model, English literacy integration