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Record W4416193414 · doi:10.1145/3773073

MobileMuseum: Smart, Portable, and Borrowable Museum Displays to Explore Interaction Patterns and Public Engagement

2025· article· en· W4416193414 on OpenAlexafffund
Alaa Nousir, Lee Jones, Fan-Cheng Lin, Tom Everrett, Sara Nabil

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2025
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsCanadian Council of Professional EngineersQueen's University
FundersSocial Sciences and Humanities Research Council of CanadaCanada Foundation for Innovation
KeywordsUser engagementPublic engagementSoftware deploymentRecreationSocial relationField (mathematics)Qualitative researchCitizen scienceSocial engagement

Abstract

fetched live from OpenAlex

We explore how smart, portable, tangible, and borrowable museum displays can be used to expand museum outreach, and provide tools for measuring user engagement. Our prototype, MobileMuseum, is a portable museum display on wheels, housing digitally-fabricated replicas of artifacts from a national science and technology museum. To track interactions without cameras, we embedded photo and magnetic door sensors and RFID tags and antenna. Our deployment study was carried out for 2 months and included 4 different locations (2 recreational and 2 educational). Data collected from 6 sensors, 261 hours of field observations, 17 filled questionnaires, and 14 interviews helped us understand interaction patterns. Qualitative and quantitative findings revealed that self-monitored interactions encouraged deeper open-ended exploration. Social sharing increased among people in groups while lower foot traffic increased engagement duration. We present generalizable opportunities for self-monitored Interfaces that lends themselves to social circulation, ‘honeybee-effect’, ‘order effect’, and ‘leftovers’ from users.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.002
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 head0.067
GPT teacher head0.328
Teacher spread0.261 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

Explore more

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