Creation and Validation of an Observational Tool to Assess Children's Domain-General Skills at Museum Exhibits
Bibliographic record
Abstract
Research on the skills children display in museums often focuses on specific domains of knowledge and ability. However, museum exhibits also may provide opportunities for children to practice domain-general skills such as collaboration, critical thinking, and confidence. This article describes the development of a 14-item observational tool (the Museum Exhibit Skills Inventory; MESI) that may be used to measure the extent to which children display some of these domain-general skills in different hands-on museum environments. In Study 1, interrater reliability for measure items was examined. In Study 2, the results of a principal component analysis suggested a 3-factor model for the MESI. In Study 3, confirmatory factor analyses revealed good fit for both a 3-factor model and a 5-factor model, and this study demonstrated strong convergent validity between the MESI and the Scale for Teachers' Assessment of Routines Engagement (McWilliam, 2000 McWilliam, R. A. (2000). Scale for Teachers' Assessment of Routines Engagement (STARE). Chapel Hill, NC: Frank Porter Graham Child Development Center, University of North Carolina at Chapel Hill. [Google Scholar]).
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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 | 0.049 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".