Constructing Chinese online course learning system based on deep learning
Year: 2025
Type: conference-paper
Abstract: Aiming at the core problems of online education in colleges and universities, such as unidirectional teaching, interactive asynchronism and evaluation linearity, this study proposes a deep learning-driven course recommendation system based on gated loop unit. By constructing a time-sensitive modeling framework for student behavior, the hierarchical attention mechanism and conversational interest extraction technology are integrated systematically and innovatively, breaking through the bottleneck of long-term dependence modeling of traditional recommendation models. At the level of algorithm design, GRU architecture is used to capture the dynamic learning track of cross-semester, and the adaptive integration of long- and short-term interests is realized through the collaborative control of update gate and reset gate. Experiments show that the AUC index of the proposed model is improved by 6.9%-7.5% compared with the traditional collaborative filtering algorithm and deep recommendation model, which verifies the effectiveness of time series modeling for feature extraction of educational data. Through B/S architecture and cognitive load optimization design, the system forms a closed-loop cognitive service system of "data acquisition - feature extraction - dynamic recommendation", which provides an innovative technical path for solving the quality dilemma of online education.
Source: Procedia Computer Science
Author: Guohui Liang
Institution: Vocational Flight College of Mianyang
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4411175769