Augmented Experiences: What Can Mobile Augmented Reality Offer Museums and Historic Sites?
Bibliographic record
Abstract
Contemporary museums are active places of edutainment where storytelling is as valuable as artefacts. Museums strive to create exciting and memorable experiences for visitors, including technophiles. This helps maintaining and attracting visitors, and therefore, financially sustains these cultural institutions. To achieve their goals, museums are increasingly exploring and adopting information and communication technologies to enhance visiting experience. Most recently, mobile augmented reality (AR) technology has found its way into museums and historic sites premises. As a medium for enriching human senses and mixing real and virtual worlds, mobile AR offers state-of-art opportunities to spatially and conceptually orient visitors. However, the growing number of museum mobile AR projects outweighs their systematic evaluations. Since designing and developing mobile AR projects consumes considerable time and resources, empirical investigation of such projects is a necessity. Although the literature recognizes this necessity, there is still a lack of systematic evaluation studies. Furthermore, among the few studies, most lack the validity that comes with employing true experiments with control and randomization. As a result, the literature lacks empirical evidence to guide future projects in the field. This research empirically investigates the application of mobile AR for enhancing visits to museums and historic sites. It focuses on the effectiveness of mobile AR for enhancing navigation and increasing informal learning and enjoyment in outdoor museums. I conducted the research investigations in two studies. For each study, I designed, co-developed, and evaluated a mobile AR prototype for use in Heritage Park, an outdoor living history museum in Calgary, Canada. The summative evaluation of each study, empirically and rigorously evaluated the objectives of the prototypes in a true experiment. Results from the experiments show that mobile AR experiences can effectively enhance museums and historic sites visiting experience by making outdoor navigation more informed and efficient as well as increasing some categories of informal learning, enjoyment, and interest. The results also show that the positive impacts of a mobile AR experience can increase intensification and gift purchases in museum gift shops. Such impact might contribute to these cultural institutions financially, and offset the costs of developing the mobile AR content.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".