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Record W3015737531 · doi:10.5430/wje.v10n2p78

Body in Drama Class: The Role of Body in Learning

2020· article· en· W3015737531 on OpenAlexvenueno aff
Necla Köksal

Bibliographic record

VenueWorld Journal of Education · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicCreative Drama in Education
Canadian institutionsnot available
Fundersnot available
KeywordsDramaPsychologyTheme (computing)Class (philosophy)PerceptionMathematics educationPedagogyVisual arts

Abstract

fetched live from OpenAlex

The aim of this study is to examine the perceptions of preservice teachers about the body in drama classes and to identify the views of preservice teachers about the role of the body on learning. As a phenomenological study, the participants consisted of 20 preservice teachers attending the English Language Teaching Department and Psychological Guidance and Counselling Department at the Faculty of Education at Pamukkale University. The data was collected via semi-structured interview form. According to the content analysis, the views of preservice teachers on the body in drama classes were categorized as a theme named "body and learning in drama class". Two sub-themes were identified under the main theme as the impending and supportive factors for learning. As a result, preservice teachers indicated that after the drama experiences their perceptions of the body has changed, they interpreted the relation of body and mind in a different way in the learning process, and comprehend the role of the body on learning better.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.004
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.001
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 head0.017
GPT teacher head0.266
Teacher spread0.249 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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