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Record W2804726225 · doi:10.5267/j.msl.2018.5.006

The effect of educational methods on creativity of pre-school children: A case study

2018· article· en· W2804726225 on OpenAlexvenueno aff
Maryam Bagherpour, Babak Shamshiri

Bibliographic record

VenueManagement Science Letters · 2018
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityMathematics educationPsychologyPedagogySocial psychology

Abstract

fetched live from OpenAlex

The aim of this study was to analyze the effectiveness of the educational method so-called "Ban Ben Bon" on language learning and creativity of pre-school children.The study was a semi-experimental one and its statistical population included 198 children studying in the preschools of Shiraz, Iran.The sample group consisted of 30 preschool children which were selected randomly.The research tool included Torrance test of creative thinking and function test including 11 questions for measuring the reading skill in preschool children.There were five preschools in Golestan town and six children were selected randomly from each five preschools.The children were asked to answer the questions of the function test, and the educating method of Ban Ben Bon was used for educating them.The results show that the educating method of Ban Ben Bon had significant effect on language learning and reading but it had insignificant effect on the creativity of preschool children.Based on statistical analysis and hypothesis testing, it was found that among four elements of creativity, including flexibility, innovation, expansion and fluidity, the method just had a significant effect on innovation.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.021
GPT teacher head0.432
Teacher spread0.411 · 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 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

Citations17
Published2018
Admission routes1
Has abstractyes

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