User Experience Evaluation in Shared Interactive Virtual Reality
Bibliographic record
Abstract
Virtual reality (VR) has served the entertainment industry all the way to world-leading museums in delivering engaging experiences through multisensory virtual environments (VEs). Today, the rise of the Metaverse fuels a growing interest in leveraging this technology, bringing along an emerging need to better understand the way different dimensions of VEs, namely social and interactive, impact overall user experience (UX). This between-subject exploratory field study investigates differences in the perceived and lived experience of 28 participants engaging, either individually or in dyads, in a VR experience comprising different levels of interactivity, i.e., passive or active. A mixed methods approach combining conventional UX measures, i.e., psychometric surveys and user interviews, as well as psychophysiological measures, i.e., wearable bio- and motion sensors, allowed for a comprehensive assessment of users' immersive and affective experiences. Results pertaining to the social dimension of the experience reveal that shared VR elicits significantly more positive affect, whereas presence, immersion, flow, and state anxiety are unaffected by the copresence of a real-world partner. Results pertaining to the interactive dimension of the experience suggest that the interactivity afforded by the VE moderates the effect of copresence on users' adaptive immersion and arousal. These results support that VR can be shared with a real-world partner not only without hindering the immersive experience, but also by enhancing positive affect. Hence, in addition to offering methodological directions for future VR field research, this study provides interesting practical insights into guiding VR developers toward optimal multiuser virtual environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".