Research on an Intelligent Learning Path Planning Method for the VR-SimTrainer Virtual Reality Training Software Based on Deep Learning - Chen, Shaocong; Wang, Zisu; Xiao, Xingyang; Yang, Yumeng; Ji, Guangyu; Liu, Wenjing (Transactions on Computer Science and Intelligent Systems Research, 2024)
This study explores an intelligent learning path planning method for the VR-SimTrainer virtual reality training software based on deep learning. With the increasing application of virtual reality technology in education and vocational training, the traditional fixed learning path training method can no longer meet the needs of personalized learning. To address this problem, this paper introduces deep learning technology to dynamically adjust the learning path by analyzing the real-time operation data and learning behavior of the trainees, providing a personalized training experience for the trainees. Experimental results show that compared with traditional path planning methods, the intelligent learning path planning method based on deep learning can significantly improve the learning effectiveness and efficiency of trainees. In addition, the research also reveals some challenges faced in the application of this method, such as the high data requirements and the limitations of model generalization ability. Overall, this study provides new ideas for the intelligent development of virtual reality training software and demonstrates the potential value of deep learning in personalized education and adaptive learning.