A Comparative Evaluation of Techniques for Sharing AR Experiences in Museums
Bibliographic record
Abstract
Museums are constantly searching for new ways to increase engagement with their exhibits, from electronic guides to modern digital technologies such as special-purpose tablets, smartphones, and virtual and augmented reality (AR). For AR exhibits in particular, promoting shared experience and group cohesion is not straightforward. In this work, we investigate scenarios in which not everyone is using a head-worn display (HWD), either because there aren't enough available or simply because someone might feel uncomfortable using it. We propose two sharing techniques for AR experiences and evaluate them in a long term in-the-wild study: Over-the-Shoulder AR, which renders a real-time virtual representation of the augmented reality content on a large secondary display; Semantic Linking, which displays contextual information about the virtual content on the same large display. We also introduce a complementary technique: Indicator Rings, which display the locations of the HWD user's objects-of-focus. We observed that participants in the Over-the-Shoulder AR and Semantic Linking conditions stayed together and exhibited more verbal exchanges than participants in a Baseline condition, which could indicate that they were more engaged. Self-reported measures indicated an increase in pair communication and increased comprehension of the virtual content for participants without the HWD. Participants without the HWD also displayed a greater understanding of the location of virtual elements with support from the Indicator Rings, and used them as a tool to guide the HWD user through the virtual content. We discuss design implications for interactive augmented reality exhibits and possible applications outside the cultural heritage scenario.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".