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Record W2978553642 · doi:10.18023/ijece.2020.26.2.006

4차산업혁명시대의 창의적 재능 발달: 유아교육현장을 중심으로

2020· article· ko· W2978553642 on OpenAlexvenueno aff
Tae-Yeon Kim

Bibliographic record

VenueEarly childhood education · 2020
Typearticle
Languageko
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

본 연구에서는 4차 산업 혁명을 위한 창의적 인재 육성을 위한 유아교육현장의 인식과 노력을 알아보고자 한다. 유치원과 어린이집 교사 71명을 대상으로 창의성 교육에 대한 설문조사를 실시하고, 설문조사 결과를 바탕으로 8명의 전문가와 초점집단면접을 진행했다. 그 결과는 다음과 같다. 첫째, 유아교사는 아동의 창의력을 중요한 교육적 가치로 간주했지만 대부분의 창의성 교육방법에 익숙하지 않았다. 둘째, 유아교육현장에서 바라본 유아 창의성 개념의 확대가 필요하다. 유아의 창의력은 결과보다는 원인에 의해 평가되어야 한다. 셋째, 교사가 유아의 창의성을 명확하게 인식하고, 창의적이며 수용적인 교실 분위기를 형성하는 것이 중요하다. 이 연구가 유아교육현장의 창의적 재능 개발에 대한 시사점을 제시 할 수 있을 것으로 기대한다.

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)0.004
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.012
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0050.028
Scholarly communication0.0120.010
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0100.002

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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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