Digital Museum Experience: Exploring Opportunities in Mixed Media Storytelling Using Augmented Reality
Bibliographic record
Abstract
<p>This research project explores opportunities for mobile augmented reality (AR) applications as an alternative means of viewing artifacts. Augmented reality is a promising technology that can greatly improve a visitor’s interactions with artifacts and their contextualized information. However, many museums hesitate to adopt AR due to concerns of gimmickry, detraction of the museum experience, and user cognitive overload (Marques & Costello, 2018). In this project, the usability and perceived usefulness of mobile AR is investigated through a comparative case study of existing AR applications for museums. Using the findings from the case study, a mixed-media storytelling mobile AR application is prototyped to demonstrate how mobile AR can enhance public engagement and improve access to cultural artifacts. By fulfilling the aim of the project, AR can be more readily accepted for museums. This project presents new insights to usability evaluations and the development of mobile AR apps for digital museum experiences. </p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".