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Record W2054329520 · doi:10.5539/jel.v2n4p208

Investigating Kindergarten Parents’ Selection of After-School Art Education Settings in Taiwan

2013· article· en· W2054329520 on OpenAlexvenueno aff
Ching‐Yuan Hsiao, Ting-Yin Kuo

Bibliographic record

VenueJournal of Education and Learning · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPatienceCreativityVisual arts educationSelection (genetic algorithm)Developmental psychologyPedagogyMathematics educationSocial psychologyThe arts

Abstract

fetched live from OpenAlex

The research purpose was to investigate kindergarten parents’ selection of after-school art education settings in Taiwan. A review of the literature and interviews with parents were conducted to identify several possible factors that would impact on parents’ selection of after-school art education settings for their children. Then, the researcher self-compiled a survey and distributed 550 copies to parents and 515 copies were obtained. The effective return rate was 93.6%. The findings were as follows: 1. The majority of parents believed children can attend after-school art education settings at the age of four years, and parents also believed that art specialists have a great impact on children’s drawings. 2. Type of kindergarten attended by children (public or private), mother’s age, mother’s occupation, and education level of mother and father have significant effects on decision making regarding the selection of after-school art education settings for their children. 3. The effects on learning: More than 85% of the parents believed that their children have achieved their expectation, which was to enrich creativity, patience, and attention. And, over 80% of the children discussed what they had learned with their parents. 4. The most popular way to teach is to combine teacher-directed and student-directed methods. 5. The most frequently used art materials are markers and crayons in after-school art education 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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.998

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)0.0030.000

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 head0.012
GPT teacher head0.246
Teacher spread0.234 · 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.

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

Citations7
Published2013
Admission routes1
Has abstractyes

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