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

被引：0

作者：

Zhou R. ^{[1
]}

机构：

[1] School of Foreign Languages, Zhanjiang University of Science and Technology, Guangdong, Zhanjiang

来源：

Applied Mathematics and Nonlinear Sciences | 2024年 / 9卷 / 01期

关键词：

Autoencoder; Deep learning; Diversity program; Japanese language education; Relevance decoder;

D O I：

10.2478/amns.2023.2.01214

中图分类号：

学科分类号：

摘要：

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. © 2023 Rui Zhou, published by Sciendo.

引用

共 18 条

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