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Record W4306658318 · doi:10.5539/jel.v11n6p119

Art-Based Kindergarten Practices and Their Coherence with Art Curriculum Objectives

2022· article· en· W4306658318 on OpenAlexvenueno aff
Rasim Başak, Cagla Erdem

Bibliographic record

VenueJournal of Education and Learning · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumVisual arts educationMathematics educationPsychologyPsychological interventionDescriptive statisticsPedagogyThe artsVisual artsMathematicsArt

Abstract

fetched live from OpenAlex

Kindergarten teachers use art-based activities and practices because they are useful and functional in learning processes and they help learning in other subject areas. While teachers allocate great deal of classroom time for art practices, an art curriculum is not required or enforced at kindergarten level. The objective of this study was to examine kindergarten curriculum, and to clarify at what extent curriculum objectives and learning outcomes met art education learning outcomes specified for elementary level art education state standards. Document content analysis and descriptive survey questionnaire were used to collect information. Content analysis method was also employed to examine classroom applications and practices. Kindergarten curriculum and visual arts curriculum were also compared and analyzed in relation to teacher responses. The study clarified a need for an art integrated approach to kindergarten. Kindergarten activities majorly consist of art-based practices, therefore kindergarten curricula should be designed based on an art-based comprehensive understanding of educational interventions.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.266
Teacher spread0.247 · 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 designQualitative
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

Citations3
Published2022
Admission routes1
Has abstractyes

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