MobileMuseum: Smart, Portable, and Borrowable Museum Displays to Explore Interaction Patterns and Public Engagement
Bibliographic record
Abstract
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".