As the demand and expectation of VR content increases, Various research and support are being carried out to improve the quality of content such as obstacles, fun, and immersion factors that should be considered in development. However, there is still a lack of research to evaluate VR content. Therefore, in this paper, we propose a method to evaluate user experience of VR contents using questionnaire, The content was selected and the questionnaire process was applied to 25 respondents, and the collected data was analyzed to verify its effectiveness. The proposed evaluation method is expected to contribute to the production of high-quality content by using it as content quality evaluation or data for cross-validation in related research.

Metaverse technology is expanding to industries in various fields, such as medical, national defense, and education, and training simulation programs have been mainstream so far. However, there have been increasing attempts to apply metaverse content to web-based platforms linked to social media services and, as a result, we face the problem of access to web-based metaverse content. Unlike traditional content, metaverse content interacts with many users, so content accessibility is the first important part to consider. In other words, to maximize the quality of metaverse content, it is essential to pull out the optimal UX through a detailed HCI (human computer interaction) design. Metaverse content development methodologies have effective methods proposed by many researchers. However, they are limited to web-based metaverse content that limits the use of high-end hardware. They are ineffective for platforms such as PCs and VR devices, as most studies focus on improving the visual performance of PCs or high-performance VR devices. Therefore, unlike existing research, the key theme of our research is to study optimized development standards that can be applied to web-based metaverse content and find out their effects through experiments. We created a development standard to be applied to a Web-based platform based on the existing metaverse content development methodology. Then, we redeveloped the VR content into the metaverse content and named them the VR build and the metaverse build. We had 25 people play virtual reality builds and metaverse builds simultaneously. Then, we measured the overall experience with an evaluation tool called the Game Experience Questionnaire (GEQ); the GEQ is a proven tool for evaluating content experiences by dividing them into positive/negative scales. When comparing the results measured from the two builds, the metaverse build showed consistent results with a higher positive scale, and a lower negative scale, than the VR build. The results showed that users indeed rated metaverse content positively. The bottom line is that the web-based metaverse content development standards that we have produced are practical. However, since generalization is limited, continuous research will be needed in more experimental groups in the future.

Virtual Reality (VR) technology is seen as one of the driving forces behind the fourth industrial revolution. It is also predicted that development of VR contents will accelerate due to 5G technology. In line with the development of these industries, research by industry, academia, and research institutes is being promoted. Problems with VR contents are in VR contents safety, and industry and academic associations are studying analysis methods and guidelines to improve problems. However, the existing research result, the experimental method, has a problem that takes a long time. These problems make it difficult for users to accumulate fatigue and obtain accurate data. Therefore, in this paper, the FGI analysis of VR experts is used to solve problems that take a long time to solve these problems. It proposes survey techniques suitable for VR contents.

For the development of virtual reality (VR) technology, research to solve the VR Sickness is essential. Many attempts have been made to measure and reduce VR Sickness using questionnaires, analysis, and bio-signals, but it is true that analytical research through clinical practice still lacks. In this paper, the researcher collected bio-signals and questionnaire data to analyze the correlation between VR Sickness and user and perform principal component analysis and chi-square independence test based on the collected data. As a result, the researcher was able to be aware of the user’s concentration and relaxation state by EEG and found they are correlated with VR Sickness. Finally, the result of the analysis was cross-checked to confirm that these correlations show a significant difference. The empirical result of this study is expected to be used for the research to reduce VR Sickness through bio-signal.

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