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Record W2778249969 · doi:10.5539/jel.v7n2p111

Exploring Creativity in Social Studies Education for Elementary Grades: Teachers’ Opinions and Interpretations

2017· article· en· W2778249969 on OpenAlexvenueno aff
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Bibliographic record

VenueJournal of Education and Learning · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityPsychologyTurkishSocial studiesCurriculumMathematics educationNature versus nurtureQualitative researchPedagogyFlexibility (engineering)Teaching methodPerceptionSocial psychologySociologySocial science

Abstract

fetched live from OpenAlex

Creativity is the critical point to developing innovative and effective citizens and children in learning social studies. The purpose of this study is to explore how creativity is promoted in social studies classrooms for young children and to research teachers’ opinions and interpretations of creativity in Turkish elementary schools. The study was conducted with in-depth interviews, teacher observation, and teacher drawings. The participants included 33 Grade -1 to Grade 4 (K1-K4) teachers, all of whom taught “life science” which is a key and preliminary subject for social studies from Grade 1 to Grade 3, and social studies for Grade 4 in central Turkey. The case study was used as a qualitative methodology. The data was analyzed using MAXQDA-11. The findings revealed that teachers were highly motivated and eager to nurture creativity in their students’ social studies learning in their implication for practice. They also indicated some obstacles regarding low-quality curriculum and instruction, teachers’ negative perceptions in promoting creativity, and finally a lack of teacher flexibility, freedom, and well-qualified professional development.

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)0.008
metaresearch head score (Gemma)0.012
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0040.008
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.002
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 head0.428
GPT teacher head0.523
Teacher spread0.095 · 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

Citations32
Published2017
Admission routes1
Has abstractyes

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