The reality of remote extended reality research: Practical case studies and taxonomy
Bibliographic record
Abstract
Remote user studies—those where the experimenter and participant are not physically located together—offer challenges and opportunities in HCI research in general, and extended reality (XR) research specifically. The COVID-19 pandemic has forced this form of research to overcome a long period of unprecedented circumstances. However, this experience has produced a lot of lessons learned that should be shared. We propose guidelines based on findings from a set of six remote virtual reality studies, by analyzing participants' and researchers' feedback. These studies ranged from one-session types to longitudinal ones and spanned a variety of subjects such as cybersickness, selection tasks, and visual search. In this paper, we offer a big-picture summary of how we conducted these studies, our research design considerations, our findings in these case studies, and what worked well and what did not in different scenarios. Additionally, we propose a taxonomy for devising such studies in a systematic and easy-to-follow manner. We argue that the XR community should move from theoretical proposals and thought pieces to testing and sharing practical data-informed proposals and guidelines.
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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 | 0.064 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".