MétaCan
Menu
Back to cohort
Record W2884077067 · doi:10.5539/jel.v7n5p102

Drama in Education for Sustainable Development: Preservice Preschool Teachers on Stage

2018· article· en· W2884077067 on OpenAlexvenueno aff
Tuğçe Akyol, Deniz Kahriman-Pamuk, Rıdvan Elmas

Bibliographic record

VenueJournal of Education and Learning · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicCreative Drama in Education
Canadian institutionsnot available
Fundersnot available
KeywordsDramaEducation for sustainable developmentSustainable developmentPedagogyPsychologyEarly childhood educationEnvironmental educationService (business)Mathematics educationPolitical scienceBusiness

Abstract

fetched live from OpenAlex

Early childhood education for sustainable development roots on environmental, socio-cultural and economic ground for encouraging lifelong learning and improving values and behaviors that support sustainable development such as use of natural resources, cultural awareness, gender equality, and democracy. Educational drama contributes to the development of skills necessary for sustainable development such as communication, cooperation and decision-making. This study has two main objectives: the former is to raise awareness and to develop these skills of pre-service teachers by organizing drama activities in Education for Sustainable Development (ESD); the later objective is to implement and to evaluate the activities based on the data collected from pre-service teachers and from one specific pre-school teacher, in whose classroom these activities were carried out. Phenomenographic approach was adapted for the current study and the data was collected through interviews, photos, and field notes. The study shows that the drama activities increase awareness and improve skills for ESD within pre-service teachers. Furthermore, opinions and experiences of the pre-service teachers and the preschool teacher state that drama has positive impact on learning of pre-school children about sustainable 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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.689
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.306
Teacher spread0.285 · 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.

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

Citations16
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Education and LearningSame topicCreative Drama in EducationFrench-language works237,207