How Museums Engage Visitors’ Self-Efficacy
Bibliographic record
Abstract
Museums have played an educational role for over two centuries. In the last 50+ years, science centres have become particularly captivating spaces for the public to physically engage with the natural world and other learning opportunities. What is not clear is if this experience is felt as fulsomely by those with low inquiry self-efficacy. Self-efficacy is the belief in one’s abilities to be successful at a particular task (Bandura, 1977). If visitors have low inquiry self-efficacy, then, for example, they may not see any purpose in applying effort to engaging with exhibits at the museum. This study attempts to understand museum visitor experiences by collecting surveys from 100 participants and interview data from 15 participants, and uses this data to characterize the AstraZeneca Human Edge exhibits at the Ontario Science Centre. This study also explores why certain exhibits elicit engagement, and tries to identify if those exhibits and features that cause low inquiry self-efficacy visitors to engage are consistent with hospitality, an ethic of welcoming all visitors, without expectation or condition of who they should be (Derrida, 2005). The research results show that: 1) the length of a visit may vary with the visitor’s inquiry self-efficacy; 2) the types of exhibits visitors interact with, and why they choose those exhibits, varies with the visitor’s inquiry self-efficacy; and 3) by considering and interpreting these differences through a conceptual lens of hospitality, I provide suggestions for how exhibit design and arrangement fosters engagement from all visitors, including low inquiry self-efficacy visitors. Better exhibit design could lead to visitor engagement that raises their inquiry self-efficacy, with lasting impacts on achieving success with their future goals. I provide suggestions for future exhibit design that may encourage engagement by making the exhibits more hospitable, that is, open to whoever the visitor is, providing them the opportunity to make their own learning in their own context.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".