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We explore the extent to which empathetic reactions are elicited when subjects view 3D motion-capture driven avatar faces compared to viewing human faces. Through a remote study, we captured subjects' facial reactions when viewing avatar and humans faces, and elicited self reported feedback regarding empathy. Avatar faces varied by gender and realism. Results show no sign of facial mimicry; only mimicking of slight facial movements with no solid consistency. Participants tended to empathize with avatars when they could adequately identify the stimulus' emotion. As avatar realism increased, it negatively impacted the subjects' feelings towards the stimuli. 

In this paper, we describe a methodology for determining audience engagement designed specifically for stage performances in a virtual space. We use a combination of galvanic skin response data (GSR), self-reported emotional feedback using the positive and negative affect schedule (PANAS), and a think aloud methodology to assess user reaction to the virtual reality experience. We describe a case study that uses the process to explore the role of immersive viewing of a performance by comparing users’ engagement while watching a virtual dance performances on a monitor vs. using an immersive head mounted display (HMD). Results from the study indicate significant differences between the viewing experiences. The process can serve as a potential tool in the development of a VR storytelling experience. 

Online learning has gained increased popularity in recent years. However, with online learning, teacher observation and intervention is lost, creating a need for technologically observable characteristics that can compensate for this limitation. The present study used a wide array of sensing mechanisms including eye tracking, galvanic skin response (GSR) recording, facial expression analysis, and summary note-taking to monitor participants while they watched and recalled an online video lecture. We explored the link between these human-elicited responses and learning outcomes as measured by quiz questions. Results revealed GSR to be the best indicator of the challenge level of the lecture material. Yet, eye tracking and GSR remain difficult to capture when monitoring online learning as the requirement to remain still impacts natural behavior and leads to more stoic and unexpressive faces. Continued work on methods ensuring naturalistic capture are critical for broadening the use of sensor technology in online learning, as are ways to fuse these data with other input, such as structured and unstructured data from peer-to-peer or student-teacher interactions.