As data become increasingly large, complex, and diverse, people need effective ways to collaboratively explore information, identify meaningful patterns, and develop shared understandings. This user study examines how people collaboratively make sense of complex, multiformat data using REVLR, an immersive 3D virtual reality environment for data exploration, pattern identification, and conceptual modeling. Participants complete REVLR tasks of varying complexity while wearing a VR headset and a non-invasive physiological sensing device. The study examines cognitive workload, usability, utility, task performance, and collaborative sense-making behaviors through physiological measures, questionnaires, observations, task performance data, and interviews. The purpose is to understand patterns of collaborative sense-making behaviors in immersive visualization and conceptual modeling systems and to explore how task complexity affects participants’ perceived workload, physiological responses, task performance, and overall experience. The findings will help advance understanding of collaborative sense-making in immersive visualization and conceptual modeling environments and inform the design of future immersive analytics systems.
Researchers and Creators
Principal Investigators:
Anita Komlodi, Associate Director, IRC; Professor, Department of Information Systems, UMBC
Karoly Hercegfi, Associate Professor, Department of Ergonomics and Psychology, Budapest University of Technology and Economics
Lee Boot, Research Associate Professor – Emeritus, UMBC; Media Artist
Students
Swathi Namburi, Graduate Student, Information Systems