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Record W2531573366 · doi:10.1080/10645578.2016.1220190

Creation and Validation of an Observational Tool to Assess Children's Domain-General Skills at Museum Exhibits

2016· article· en· W2531573366 on OpenAlexfundno aff
Gregory S. Braswell

Bibliographic record

VenueVisitor Studies · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsnot available
FundersMinistère de l'Économie, de la Science et de l'Innovation - Québec
KeywordsChapelConfirmatory factor analysisPsychologyObservational studyInter-rater reliabilityScale (ratio)Convergent validityMathematics educationRating scaleDevelopmental psychologyPsychometricsStructural equation modelingComputer scienceGeographyStatisticsCartographyMathematics

Abstract

fetched live from OpenAlex

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]).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.075
GPT teacher head0.307
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2016
Admission routes1
Has abstractyes

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