BlueVR: Design and Evaluation of a Virtual Reality Serious Game for Promoting Understanding towards People with Color Vision Deficiency
Bibliographic record
Abstract
People with color vision deficiency (CVD) often encounter color-related challenges in their daily life, which are difficult for those with non-CVD to comprehend fully. Therefore, we designed a Virtual Reality (VR) serious game, BlueVR , to simulate challenging scenarios encountered by people with CVD and facilitate understanding from people with non-CVD. We conducted an empirical study with thirty participants with non-CVD and six participants with CVD to evaluate the opportunities and challenges of BlueVR . Our findings suggest that BlueVR increased people with non-CVD’s understanding, awareness, and perspective-taking abilities towards people with CVD. Moreover, interviews with participants with CVD revealed that BlueVR accurately depicts their real-life discomforts and meets their expectations to improve potential social awareness. This research contributes valuable insights into the mechanisms underlying the effectiveness of VR serious games in promoting understanding and design implications for future game development.
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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 | 0.003 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".