NSF CAREER Smart Learning in Multi-person VR
Immersive multi-person virtual reality research using multimodal analysis of physiological measures.
NSF
NSF CAREER Smart Learning in Multi-person VR
This project studies how physiological measures can be analyzed in near real time to understand learner engagement in fully immersive multi-person virtual reality.
- nsf
- virtual reality
- smart learning
- multimodal
- eye tracking
Project scope
Non-text-based smart learning in immersive multi-person VR using eye movements, brain activities, and haptic interactions.
- Non-text-based smart learning refers to technology-supported learning that uses visualized information and adapts material to individual needs.
- The work focuses on eye movement characteristics, haptic interactions, and brain activities to support prediction and timely scaffolding during learning.
Researchers
- Dr. Ziho Kang
- Ricardo Palma Fraga
- Junehyung Lee
- Collaborators at Drexel University and Kent State University
Data availability
The dataset for this project is hosted externally due to its size.
Access dataset on ZenodoProject video
