10:00 AM - 10:15 AM
[G01-05] Implementing Metaverse Fieldwork for Coastal Landform -Spatio-Temporal Analysis using VR users Behavior Data-
Keywords:VR, UAV, Metaverse, Cluster Analysis, Remote Sensing, Geographical Education
Online lectures and conferences have become possible through online tools for preventing COVID-19, but the lack of communication in one-way communication using these tools has caused dissatisfaction among many. On the other hand, field trips are considered important educational opportunities in earth science and related fields, but these are also restricted by the constraints of the coronavirus. Currently, new initiatives related to earth science and geography education are being explored, such as virtual field trips using 360-degree images and videos. However, these do not allow for free spatial movement and interaction with others is also restricted during the activity. We need to seek a new method that can improve the participants satisfaction.
The advent of the immersive VR system has accelerated the users experience to join the Metaverse: where multiple users can join a collaborative virtual environment, interact, and communicate with each other. The possibility of the metaverse fieldwork may transcend the constraints of conventional virtual tours, such as the one-person experience method and static point observation. Therefore, the purpose of this study is to characterize the spatio-temporal distribution of user behavior data and to explore the potential of metaverse fieldwork in relation to geographical education.
The research method is as follows:
1. Reproduce the physical environment as a 3D model by utilizing a UAV and the photogrammetry approach.
2. Reconstruct the reproduced 3D model in VR space and design the world for educational purposes.
3. Conduct an actual metaverse fieldwork with the participation of general VR users.
4. Analyze users' behavior in the metaverse and verify educational effects by analyzing spatio-temporal data and conventional questionnaire surveys.
Conventionally, questionnaires are the mainstay of the fieldwork, however we analyze the effectiveness of the education by utilizing the advantages of the VR system, by visualizing the users quantitative behavioral data. The results of the analysis and the actual metaverse world created will be presented.
Acknowledgements
We thank the VRC Science Assembly team together with cocutan (@cocu_tan) for providing the tool for the behavior analysis. We also thank all the VR users participated in the actual fieldwork and accepting to provide the data.
The advent of the immersive VR system has accelerated the users experience to join the Metaverse: where multiple users can join a collaborative virtual environment, interact, and communicate with each other. The possibility of the metaverse fieldwork may transcend the constraints of conventional virtual tours, such as the one-person experience method and static point observation. Therefore, the purpose of this study is to characterize the spatio-temporal distribution of user behavior data and to explore the potential of metaverse fieldwork in relation to geographical education.
The research method is as follows:
1. Reproduce the physical environment as a 3D model by utilizing a UAV and the photogrammetry approach.
2. Reconstruct the reproduced 3D model in VR space and design the world for educational purposes.
3. Conduct an actual metaverse fieldwork with the participation of general VR users.
4. Analyze users' behavior in the metaverse and verify educational effects by analyzing spatio-temporal data and conventional questionnaire surveys.
Conventionally, questionnaires are the mainstay of the fieldwork, however we analyze the effectiveness of the education by utilizing the advantages of the VR system, by visualizing the users quantitative behavioral data. The results of the analysis and the actual metaverse world created will be presented.
Acknowledgements
We thank the VRC Science Assembly team together with cocutan (@cocu_tan) for providing the tool for the behavior analysis. We also thank all the VR users participated in the actual fieldwork and accepting to provide the data.