Educational Virtual Field Trips based on Social VR and 360Β° Spaces
September 09, 2024 Β· Declared Dead Β· π International Conference Games and Learning Alliance
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Surya Kalvakolu, Heinrich SΓΆbke, Jannicke Baalsrud Hauge, Eckhard Kraft
arXiv ID
2409.05496
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.MM
Citations
1
Venue
International Conference Games and Learning Alliance
Last Checked
4 months ago
Abstract
Virtual field trips (VFTs) have proven to be valuable learning tools. Such applications are mostly based on 360Β° technology and are to be characterized as single-user applications in technological terms. In contrast, Social VR applications are characterized by multi-user capability and user-specific avatars. From a learning perspective, the concepts of collaborative learning and embodiment have long been proposed as conducive to learning. Both concepts might be supported using Social VR. However, little is currently known about the use of Social VR for VFTs. Accordingly, the research questions are to what extent VFTs can be implemented in Social VR environments and how these Social VR-based VFTs are perceived by learners. This article presents an evaluation study on the development and evaluation of a VFT environment using the Social VR platform Mozilla Hubs. It describes the design decisions to create the environment and evaluation results from a mixed-method study (N=16) using a questionnaire and focus group discussions. The study highlighted the opportunities offered by Social VR-based VFTs but also revealed several challenges that need to be addressed to embrace the potential of Social VR-based VFTs to be utilized regularly in education.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted