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<p>This paper focuses on the development and construction of language teaching in higher vocational colleges and universities under the perspective of deep learning, proposes the use of multimedia courseware to update the course content, interactive teaching to optimize knowledge transfer and skills training, and the creation of an interactive training environment for the Russian language. Analyze the language learning environment of higher vocational colleges under deep learning, clarify the direction of Russian language teaching, use wavelet transform algorithm to compress and process Russian language teaching resources, and enrich the teaching construction. Classify language classroom teaching interaction behaviors, clarify the criteria for judging teaching interaction behaviors, put forward connection timing classification technology under deep learning, and construct classroom interaction behavior recognition based on speech recognition. Combining video samples to analyze the classroom interactive behaviors of Russian language teaching, analyzing the use of Russian digital teaching resources, and evaluating the advantages of the reform of the Russian Audiovisual Speaking course in the light of students’ satisfaction with the reform of the course. In the interactive Russian language classroom teaching, the proportion of teacher’s and student’s speech is 24.24% and 30.205%, respectively. The proportion of open-ended questions and closed questions is 4.68% and 3.67%, respectively, and 83.62% of the students are satisfied with the classroom teaching of Russian Audiovisual and Speaking, so it can be seen that the construction and reform of the Russian language course forms a classroom that takes the students as the main body and the teacher as the auxiliary teaching This shows that the construction and reform of the Russian language course forms a classroom with students as the main body and teachers as the auxiliary teaching, which is in line with the needs of Russian language teaching.</p>
</abstract>

This paper focuses on the development and construction of language teaching in higher vocational colleges and universities under the perspective of deep learning, proposes the use of multimedia courseware to update the course content, interactive teaching to optimize knowledge transfer and skills training, and the creation of an interactive training environment for the Russian language. Analyze the language learning environment of higher vocational colleges under deep learning, clarify the direction of Russian language teaching, use wavelet transform algorithm to compress and process Russian language teaching resources, and enrich the teaching construction. Classify language classroom teaching interaction behaviors, clarify the criteria for judging teaching interaction behaviors, put forward connection timing classification technology under deep learning, and construct classroom interaction behavior recognition based on speech recognition. Combining video samples to analyze the classroom interactive behaviors of Russian language teaching, analyzing the use of Russian digital teaching resources, and evaluating the advantages of the reform of the Russian Audiovisual Speaking course in the light of students’ satisfaction with the reform of the course. In the interactive Russian language classroom teaching, the proportion of teacher’s and student’s speech is 24.24% and 30.205%, respectively. The proportion of open-ended questions and closed questions is 4.68% and 3.67%, respectively, and 83.62% of the students are satisfied with the classroom teaching of Russian Audiovisual and Speaking, so it can be seen that the construction and reform of the Russian language course forms a classroom that takes the students as the main body and the teacher as the auxiliary teaching This shows that the construction and reform of the Russian language course forms a classroom with students as the main body and teachers as the auxiliary teaching, which is in line with the needs of Russian language teaching.

Construction and Reform of the Russian Audiovisual Speaking Course in Higher Vocational Institutions in the Context of Deep Learning

Applied Mathematics and Nonlinear Sciences

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

This paper focuses on the development and construction of language teaching in higher vocational colleges and universities under the perspective of deep...

Construction and Reform of the Russian Audiovisual Speaking Course in Higher Vocational Institutions in the Context of Deep Learning

Data publikacji: 05 sie 2024

Zakres stron: -

Otrzymano: 01 kwi 2024

Przyjęty: 26 cze 2024

DOI: https://doi.org/10.2478/amns-2024-2057

Słowa kluczowe
Wavelet transform algorithm, Connection timing classification, Teaching resources, Interactive behavior, Russian language teaching

© 2024 Yanrui Huang, published by Sciendo

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

Construction and Reform of the Russian Audiovisual Speaking Course in Higher Vocational Institutions in the Context of Deep Learning

Data publikacji: 05 sie 2024

Zakres stron: -

Otrzymano: 01 kwi 2024

Przyjęty: 26 cze 2024

DOI: https://doi.org/10.2478/amns-2024-2057

Słowa kluczoweWavelet transform algorithm, Connection timing classification, Teaching resources, Interactive behavior, Russian language teaching

© 2024 Yanrui Huang, published by Sciendo

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

Słowa kluczowe
Wavelet transform algorithm, Connection timing classification, Teaching resources, Interactive behavior, Russian language teaching