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

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.