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Record W1680990973 · doi:10.11575/prism/25067

Augmented Experiences: What Can Mobile Augmented Reality Offer Museums and Historic Sites?

2015· dissertation· en· W1680990973 on OpenAlex
Rozhen Kamal Mohammed-Amin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePRISM (University of Calgary) · 2015
Typedissertation
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsnot available
Fundersnot available
KeywordsAugmented realityHuman–computer interactionComputer science

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.571
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.240
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it