THE MULTIPLE ROLES OF EXHIBIT LEARNING IMPACT ASSESSMENTS IN A SCIENCE CENTRE
Bibliographic record
Abstract
Visitor’s engagement with an exhibit could be considered as an important learning indicator and predictor. Based on this idea, Barriault and Pearson (2010) proposed an assessment framework, which has been constantly used by a Canadian Science Centre - Science North for different areas and proposes since 2005. This paper presents and analyses this experience as well as discuss some features of this framework as accuracy, feasibility and adaptability for other contexts. Documental analysis and interviews with Science North science staff and directors were held in order to deeply understand how this assessment method has been used at this Science Centre over these 10 years. All interviewees pointed that data collected through this method has been used to make changes in an exhibit in order to improve visitor's engagement, in floor-staff training programmes, as an important information for international partnerships and sales and, more recently, as a Centre's management indicators. In addition, all respondents stressed the accuracy and feasibility as strengths of the tool as far as data collected is easy to understand and "make sense" for all staff. Science North's experience shows that collecting and analysing learning data can play an important role in providing useful findings for different areas of a science centre, and could be an important way to improve visitor’s experience at the museum.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".