Work

Application of mobile learning system based on convolutional network technology in students’ open teaching strategies

Year: 2025

Type: article

Abstract: This study designs and develops a mobile learning system based on a convolutional neural network to support open teaching strategies. By integrating a temporal convolutional network (TCN), dilated causal convolution (DCC), and reinforcement learning (RL), the study proposes a TCN-DCC-RL model for personalized learning resource recommendations. The model is evaluated using the learning analytics dataset from the UK Open University. Experimental results show that the TCN-DCC-RL model achieves high performance, with an accuracy of 96.49%, F1-score of 90.57%, Mean Average Precision of 92.27%, and Normalized Discounted Cumulative Gain (NDCG@20) of 0.938. These findings demonstrate that the proposed model significantly enhances the personalization and intelligence of learning resource recommendations, offering a novel technical approach for optimizing future intelligent education systems.

Source: Scientific Reports

DOI: 10.1038/s41598-025-25532-0

Authors: Chang Yu, Xue Yu

Institutions: Catholic University of Korea, Anhui University of Finance and Economics

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $2,690