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<p>This paper constructs a diversified Japanese language course recommendation model based on the autoencoder network model using deep learning, which includes stack autoencoder, self-attention mechanism encoder, and relevance decoder. A system for evaluating the quality of Japanese language education was constructed using hierarchical analysis. The correlation between the Japanese listening test and the degree of innovation of diversified Japanese language education courses and the influence of the innovation of diversified Japanese language education courses on students’ Japanese language performance were analyzed, respectively. The study showed that the correlation coefficient between the Japanese listening test score and the diversified Japanese language education program was 0.865, and the students who received the diversified Japanese language education program scored 7 points higher than the students who received the traditional Japanese language education program.</p>
</abstract>

This paper constructs a diversified Japanese language course recommendation model based on the autoencoder network model using deep learning, which includes stack autoencoder, self-attention mechanism encoder, and relevance decoder. A system for evaluating the quality of Japanese language education was constructed using hierarchical analysis. The correlation between the Japanese listening test and the degree of innovation of diversified Japanese language education courses and the influence of the innovation of diversified Japanese language education courses on students’ Japanese language performance were analyzed, respectively. The study showed that the correlation coefficient between the Japanese listening test score and the diversified Japanese language education program was 0.865, and the students who received the diversified Japanese language education program scored 7 points higher than the students who received the traditional Japanese language education program.

Diversified Curriculum Innovation for Japanese Language Education in Colleges and Universities under the Deep Learning Model

Zhanjiang University of Science and Technology

Applied Mathematics and Nonlinear Sciences

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

This paper constructs a diversified Japanese language course recommendation model based on the autoencoder network model using deep learning, which includes...

Diversified Curriculum Innovation for Japanese Language Education in Colleges and Universities under the Deep Learning Model

Pubblicato online: 22 nov 2023

Pagine: -

Ricevuto: 18 gen 2023

Accettato: 02 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01214

Parole chiave
Deep learning, Autoencoder, Relevance decoder, Japanese language education, Diversity program

© 2023 Rui Zhou, published by Sciendo

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

Diversified Curriculum Innovation for Japanese Language Education in Colleges and Universities under the Deep Learning Model

Pubblicato online: 22 nov 2023

Pagine: -

Ricevuto: 18 gen 2023

Accettato: 02 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01214

Parole chiaveDeep learning, Autoencoder, Relevance decoder, Japanese language education, Diversity program

© 2023 Rui Zhou, published by Sciendo

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

Parole chiave
Deep learning, Autoencoder, Relevance decoder, Japanese language education, Diversity program