The Virtual Reality Questionnaire Watch: Exploring Novel Methods Integrated with Google Forms
Bibliographic record
Abstract
We developed a Questionnaire Toolkit (QT) that embeds Google Forms questionnaires into a virtual reality (VR) environment (in-VRQs).It provides a novel implementation of in-VRQs and helps lower the entry barrier to VR research by providing researchers with an experimental task setup to conduct studies.We evaluated the QT's self-reported presence, task load, and usability compared to Google Forms on a tablet.We found significant differences in task load measures' presence and physical demand subscale items.Participants preferred using the in-VRQs generated by the QT for their VR applications because it was impossible to use out-VRQs due to the equipment users wore.When asked about their sense of presence, they reported heightened presence returning to VR tasks when using the QT.We conducted walkthroughs to evaluate the QT deployment.The walkthrough results uncovered usability issues and showed that researchers could deploy the toolkit on their devices with the developer's help.
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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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".