Facilitators Improve the Learning Experience of Visitors to a Science Centre
Bibliographic record
Abstract
We studied how interactions with interpretative science centre staff impacts the learning behaviours and engagement levels of visitors who engage with exhibits at Science North (Sudbury, Canada). This study uses the Visitor-Based Learning Framework. The tool consists of seven discrete learning-associated behaviours that visitors show when engaging with exhibits, which are grouped into three categories of engagement: Initiation, Transition, and Breakthrough. These categories reflect increasing levels of engagement and depth of the learning experience. We studied forty-seven Science North exhibits, and 4,835 visitors to analyse the impact of unstructured facilitation in a naturalistic setting. We compared visitor Engagement Levels with and without a facilitator present. We determined that the presence of staff has a statistically significant impact on the percentage of visitors that engage in Breakthrough behaviours. When a facilitator is present, more visitors reach the Breakthrough Level of Engagement ( p < 0.001). In the second phase of the study, we explored what facilitators do and say through thematic analysis to uncover common patterns of facilitator actions and comments. Our findings showed that facilitators employed strategies and methods that can be grouped in four categories or Facilitation Dimensions: Comfort, Information, Reflection, and Exhibit Use. These dimensions encompass different strategies and techniques of facilitation, that are used in a variety of situations and sequences. Our study goes beyond anecdotal evidence to show that staff-visitor interactions have a positive impact on visitor engagement with exhibits and therefore, potentially on visitor learning from exhibits. Our findings can be used to inform not only training programs but also managerial decisions and considerations around resource allocation. We suggest that facilitators are a fundamental asset for institutions that prioritize visitor engagement, one that should be given top priority when considering areas for investing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".