Abstract

VR technology can help create optimal virtual learning spaces. Such spaces offer new visual experiences that break through the limitations of time and space and greatly stimulate people’s imagination and creativity in learning. Currently, the bandwidth required for such spaces limits the large-scale application of virtual reality (VR) technology for this purpose. With the large-scale deployment and application of high-speed networks, however, online education platforms based on VR technology will be better able to meet the diversified and personalized learning needs of learners. To promote the development and popularization of new online education platforms based on VR, the factors influencing the migration of learners from traditional online education platforms to new platforms need to be understood more clearly. A model based on the theory of negative, positive, and anchoring effects can explain learners’ migration behavior in this connection. To this end, a structural equation model based on the PLS variance algorithm was used to analyze data obtained through offline and online questionnaires. It was found that in terms of “negative effects”, the afunction and loyalty associated with traditional online education platforms reduced learners’ willingness to migrate to new platforms based on VR technology. In terms of “positive effects”, the novel interactivity and personalization brought by the new platform increased the willingness of users of traditional platforms to migrate to new platforms. In terms of “anchoring effects”, the system quality and relationship quality of learners’ use of traditional online education platforms, as well as the transfer costs associated with the new platform, generated learners’ risk perception about platform migration. In addition, risk perception not only negatively affects learners’ migration to the new platforms, but also strengthens their cognition of the system quality and relationship quality of the traditional platforms, while reducing their interactive awareness of those platforms. Therefore, by adjusting the psychological component of virtual learning, the online education platforms based on VR technology can create high-quality platforms migrating from traditional platforms.

Highlights

  • With the emphasis on lifelong learning becoming an important trend, online education, far, has been treated as a kind of web-based mode of distance education [1]

  • When the online education platforms based on virtual reality (VR) technology are made available, and the learners who have not yet migrated to them observe the rich new functions they afford, this can produce a kind of envy-based risk perception, which causes the feeling that traditional online education platforms are lacking

  • The application of VR technology to online education platforms can improve the efficiency of online learning through individual portraits, visual recognition, voice recognition, and other innovations, so as to meet the diversified and personalized learning needs of learners and in the process bring unprecedented changes to the field of education

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Summary

Introduction

With the emphasis on lifelong learning becoming an important trend, online education, far, has been treated as a kind of web-based mode of distance education [1]. The factors that might affect learners’ use of online education platform based on VR technology are complex, and include the shortage of traditional platforms, the advantages of new platforms, and cognitive risk perceptions [22]. There has been no research on the mechanisms explaining learners’ migration from traditional online education platforms to VR-based platforms. This gap may hinder research on and the development of new platforms, while restricting the application, promotion, and popularization of platforms using VR technology.

Migration Behavior and “Negative-Positive-Anchor” Theory
VR Technology and Its Potential Applications in the Field of Education
Negative Effects
Positive Effects
Anchoring Effects
Measurement of Variables
Data Collection
Measurement Model
Structural Model
Analysis of Mediating Effects
Conclusions
Suggestions for Future Applications
Full Text
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