The study of virtual reality adaptive learning method based on learning style model

Abstract

Learning style is the endogenous cause of students' unique behaviors when they are performing learning tasks. The adaptive learning system that considers learning style can provide a personalized experience to stimulate students' enthusiasm, which had been widely studied in recent years. However, most of such existing systems are constructed based on a desktop environment, which leads to the less‐than‐ideal effect of personalized learning due to the limitation of interaction means and environmental dimension. Therefore, an adaptive virtual reality learning method based on the learning style model was proposed in this study. This method continuously iterated the identification of learning style based on students' subjective and objective data. Then, the content of virtual learning environment was sustainedly adjusted according to the identification results, thus enabling the environment to dynamically adapt to students' learning styles. To evaluate the feasibility of this proposed method, a controlled experiment on 152 participants was conducted. Results show that this method obtained relatively stable and accurate results of learning style identification, with an accuracy range of 74.38%–80.30%. Moreover, learning in the virtual environment constructed based on this method had a positive impact on students' learning motivation and outcomes.

Experiences of web‐based extended reality technologies for physics education

“…Other works related to physics have presented similar results in learning motivation when using XR technologies such as AR and VR for physics education [27, 34], where they used characteristics such as interactive interfaces, presentation of the learning content, and even consider the learning styles (such as visual, auditory and kinesthetic) to measure its effects in students motivation.…”

Fast and accurate multiscale feature fusion stereo matching network

“…The whole model is trained supervised end-to-end using the datasets and the final loss is calculated by smoothing L1 loss as the basis, the loss function used is shown in equation (3).…”

“…Binocular stereo matching uses image information from multiple viewpoints to infer the depth information of the scene by calculating the disparity of different regions in the image, thus achieving the perception and understanding of three-dimensional space. This technique not only has a wide range of applications in 3D reconstruction, autonomous driving, and robot navigation but also plays an important role in augmented reality and industrial quality inspection [1][2][3] . In autonomous driving systems, the use of stereo matching enables a deep perception of the environment and helps the vehicle perform tasks such as obstacle detection, road recognition, and navigation.…”

A Study of Online Learning Context Optimization Strategies under the Metaverse Perspective

“…Virtualization is to provide immersive experience based on technologies such as virtual reality and augmented reality, to generate a mirror image of the real world based on digital twin technology, to build a social and economic system based on blockchain technology, and finally to achieve close integration between the virtual world and the real world in terms of economic system, social system and identity system [7][8][9].…”