Challenges of Mobile Augmented Reality in Museums and Art Galleries for Visitors Suffering From Vision, Speech, and Learning Disabilities
Bibliographic record
Abstract
In Today's digital world, AR is a tech which imposes layers of virtual segments on the real world. Research Practitioners and Designers in all applications seem to be more concerned about the learning facilities than keeping the visitors engaged in public art exhibitions, Museums, and holiday tourist locations. These ignored circumstances have provoked studies to emphasize more on the usability of Mobile Augmented Reality (M.A.R.) at Art galleries and Museums. According to the recent surveys, the current M.A.R. applications at target locations focus on healthy people without any disabilities, and not on those with disabilities. This chapter recommends major design elements of M.A.R. at museums and art galleries, and highlights all the challenges faced by visitors suffering from visual, speech, and Learning Disorders. The research discusses the 11 vital elements which include Usability, Design, Motivation, Interaction, Perceived control, Satisfaction, Attention, and others involving engagement of M.A.R. necessary for building an effective M.A.R. application for disabled people.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".