Individualized Visits to Foster the Engagement and the re-visit in Museums
Bibliographic record
Abstract
Museums have become places that, besides conserving and storing artefacts, provide visitors with education and amusement. They now have to compete with the entertainment industry to attract visitors and expand their audience. The use of digital technology is emerging as a solution.While many studies focus on the visitor side, we are interested in tools for museum staff. To understand their needs and processes, we adopted a participatory and iterative design process, involving museum professionals as end-users at each step (i.e. user observation, design, prototyping, users tests). We conducted 7 meetings, 4 interviews studies and 2 experimental observations with 12 museum experts (communication, IT, public and content experts) from 5 institutions (Exhibition Centres, Science Centre, Archaeological Museum, Museum of Fine Arts).Our analysis revealed two issues faced by these museums. First, despite the recommendation of institutional documents, visitors service is almost never involved before the end of the exhibition design process. Thus, they have no mean to shape the scenography in order to adapt it to visitors. Second, we identified a strong need for encouraging local visitors’ engagement and revisit. Diversifying the visits is a solution considered by museums, but relying on temporary exhibits is too costly for small museums and creating thematic visits is not participatory enough.The creation of individualized visits, allowing visitors to explore existing exhibition on their own depending on their needs and desires, meets both challenges. We thus focus on the design of tools that empower museum professionals to create such visits. We identified that museums need, first, to collect more information about their visitors and, second, to be able to create, evolve and maintain the solutions on their own according to visitors needs. Our aim is to design a tool which respects these two key points and could enable the creation of personalized and dynamic museum visits.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".