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Record W4413903726 · doi:10.5539/jel.v15n1p101

Evaluating Children’s Creativity Through Drawing: Designing and Validating an Assessment Tool for Originality

2025· article· en· W4413903726 on OpenAlexvenueno aff
Yanfei Xu, Mohd Nazri Abd Rahman

Bibliographic record

VenueJournal of Education and Learning · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsOriginalityCreativityPsychologyMathematics educationEvaluation methodsPedagogySocial psychologyEngineering

Abstract

fetched live from OpenAlex

The purpose of this study is to design an assessment tool for measuring the originality dimension of children’s creativity through their drawings. The tool was developed based on in-depth interviews with 9 experts, whose feedback led to the identification of the core dimensions and scoring criteria. The tool evaluates children’s drawings across four key dimensions: creativity, number of details, color usage, and composition complexity, to assess the originality of their creativity. To assess the effectiveness of the tool, an experimental method was employed, using pre- and post-assessments of 23 children in the experimental group and 21 children in the control group during nature play activities. Changes in children’s originality were measured through these assessments, with drawings evaluated across the four dimensions. The results indicate that the tool effectively captures changes in children’s originality, providing strong support for fostering creativity in early childhood education and offering practical implications for real educational settings.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.068
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.444
Teacher spread0.359 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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