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Our overarching goal is to invesitgate human's decision making processes to increase performance, inform the design of systems, and develop training intervention methods. Our goal is obtained through developing pattern analysis algorithms to analyze physiological measures, such as eye movements, haptic interactions, and brain activities.

Key words: Human factors, human-computer interaction, cognitive engineering, human-systems engineering, pattern analysis, UX research, human-centered design.

Announcement: Dr. Kang wins the National Science Foundation CAREER Award!

CAREER: Non-text-based smart learning in fully immersive multi-person Virtual Reality using near real-time multimodal analysis of physiological measures

Short description: Non-text-based smart learning refers to technology-supported learning that uses non-text features (i.e. visualized information) and adapted learning materials based on the individual’s needs. Fully immersive multi-person virtual reality (MVR) refers to humans located at different places wearing VR devices to join a single virtual room, a classroom with unlimited size, to learn from an instructor. The learning environment is poised to undergo a major reformation, and MVR will augment, and possibly replace, the traditional classroom learning environment. The purpose of this research is to discover new smart learning methodologies within the MVR environment using nonintrusive multimodal analysis of physiological measures, including eye movement characteristics, haptic interactions, and brain activities.


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