Work

An Intelligent Classification Method for Online Chinese Language Teaching Resources in Higher Education Based on Deep Reinforcement Learning

Year: 2025

Type: article

Abstract: In modern higher education, especially in college Chinese Teaching (ChT), online resources play a crucial role in enhancing teaching content and effectiveness. However, managing and utilizing these resources efficiently remains a challenge, particularly in data classification. Traditional classification algorithms often lack accuracy and struggle to handle large, complex datasets. This study addresses these issues by applying Deep Reinforcement Learning (DRL), specifically a Deep Q-Network (DQN), to improve the classification of online ChT resources in universities. To enhance performance, an optimized reward mechanism is designed to automatically learn the best classification strategy for extensive teaching materials. Experimental results show that the proposed DRL-based approach outperforms competitive classification techniques across multiple performance metrics. This research not only introduces an innovative method for managing online ChT resources but also expands the application of DRL in education.

Source: International Journal of High Speed Electronics and Systems

DOI: 10.1142/s0129156425404838

Authors: Lei Song, Yuan Zhu

Institutions: Shaanxi Fashion Engineering University, Xi'an Jiaotong University

Language: English

Topic: Educational Technology and Pedagogy

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Funder: Ministry of Education of the People's Republic of China