Construction of a Basic Japanese Teaching Resource Base Based on a Deep Neural Network under a Big Data Environment

Area of Science:

• Educational Technology
• Computer Science
• Linguistics

Background:

• The

Purpose of the Study:

• To address challenges in university education posed by "Internet plus" by developing a professional Japanese teaching resource base.
• To establish and enhance a mechanism for constructing a diversified, systematic, open, and long-term Japanese instructional resource base.
• To examine issues in building a Japanese teaching resource database in the context of rapid IT industry expansion.

Main Methods:

• Development of a fundamental Japanese teaching resource database utilizing Deep Neural Network (DNN) architecture.
• Application of Convolutional Neural Network (CNN) technology to create an Arduino device identification program for accessing learning resources.
• Integration of gadgets into the learning process via the Arduino device identification program.

Main Results:

• The developed Japanese teaching resource database system achieved a precision rate of approximately 93% and a recall rate of about 94%.
• The system demonstrated superior performance compared to conventional teaching resource systems.
• The system is capable of providing high-quality teaching resources for fundamental Japanese language instruction.

Conclusions:

• The DNN-based Japanese teaching resource database, enhanced by CNN technology for device identification, effectively addresses the need for advanced educational resources.
• The system's high precision and recall rates indicate its potential to significantly improve the quality and accessibility of fundamental Japanese language learning.
• Continuous updating and improvement of instructional resources are crucial for the diversified, systematic, open, and long-term development of Japanese teaching materials.