Research Methodologies across the Physical - Virtual Reality Spectrum
Bibliographic record
Abstract
Over the last couple of years, there has been a big push toward making immersive and mixed technologies available to the general public. Yet, designing for these new technologies is challenging, as users need to position virtual objects in 3D space. The current state-of-the art technologies used to access these virtual environments (e.g., Head-mount displays (HMD)s also presents additional challenges for designers when considering depth perception issues that affect user precision. Moreover, these challenges are exacerbated when designers consider accessibility needs of special populations. To make new immersive and mixed technologies more accessible, we propose a tutorial at UbiComp / ISWC 2023 to discuss design strategies, research methodologies, and implementation practices with special populations using technologies across the physical-virtual reality spectrum. In this tutorial participants will learn how to make these technologies more accessible by (1) teaching students of the tutorial how to design, prototype, and evaluate these technologies using empirical research. We aim to (2) bring together researchers, practitioners, and students who are interested in making immersive and mixed technologies more accessible and (3) identify common problems when designing new user interfaces.
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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 | 0.034 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".