The impact of staff interaction in the learning experience of visitors to a science centre: an initial framework for facilitation
Bibliographic record
Abstract
The purpose of this study is to investigate how the interaction with interpretative science centre staff impacts the learning of visitors who engage with exhibits at Science North (Sudbury, Ontario). Science North is a science center in which tailoring the learning experience for each visitor is of paramount importance. Staff and volunteers are affectionately known as Bluecoats, in reference to the bright blue lab coats they wear on the exhibition floors. Given the current understanding of unstructured visitor-staff interactions, it becomes evident that there is a need to further explore this rich and complex field, paying special attention to behaviours and attitudes of staff that are conducive to learning, independently of the particulars of an exhibit or its science topic. Although researchers agree that learning happens in museums, and that staff play a meaningful role, assessing the impact visitor-staff interactions can be difficult and costly (Barriault & Pearson, 2010). For this reason, this study uses the Visitor Engagement Framework, a practical tool based on constructivist learning theories, which is effective in assessing the learning potential of exhibits. In this framework, Breakthrough behaviours are observable behaviours and activities which reflect that the visitor is fully engaged and committed to the learning experience and recognizes its relevance to their personal life (Barriault & Pearson, 2010). This study has two complementary phases. In the quantitative phase, the goal is to determine what impact (if any) do Bluecoats have on visitors’ learning behaviours. Using the Visitor Engagement Framework, we will compare the visitor engagement levels of multiple exhibits, with and without a Bluecoat present, paying special attention to the difference in the percentage of visitors that reach Breakthrough in each condition. In the qualitative phase, through an analysis of emergent themes, the goal is to explore what Bluecoats do and say to have that impact. The presence of a Bluecoat has a clear, quantifiable, statistically significant impact on the percentage of visitors that engage in Breakthrough behaviours. When a Bluecoat is present, more visitors engage in Breakthrough behaviours. To produce this impact, Bluecoats resort to strategies and methods that can be grouped in 4 categories or Dimensions: Comfort, Information, Reflection, and Exhibit Use. These dimensions encompass different strategies and techniques of facilitation, all equally useful and powerful. A rich learning experience means that Bluecoats resort to many different strategies, in a variety of sequences, tailored to each visitor and exhibit. Furthermore, this framework can serve as an assessment tool for science centres, to help them better understand how their staff can make the visitors’ learning experiences richer.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".