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Record W2891925773 · doi:10.12795/cp.2017.i26.10

El juego dramático como estrategia de inclusión y de educación emocional: evaluación de una experiencia en educación infantil

2017· article· es· W2891925773 on OpenAlexaff
Bárbara Tejado Cabeza

Bibliographic record

VenueCuestiones Pedagógicas · 2017
Typearticle
Languagees
FieldSocial Sciences
TopicLiteracy and Educational Practices
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsHumanitiesSociologyPhilosophy

Abstract

fetched live from OpenAlex

El objetivo de este artículo es presentar los resultados de la evaluación de una experiencia educativa que introduce el juego dramático como estrategia para promover el trabajo de la inclusión y las emociones en Educación Infantil. Al mismo tiempo, se desea profundizar en el papel qué ha de desempeñar el docente para el logro de una educación inclusiva de calidad orientada al desarrollo emocional en la primera infancia. Para ello se ha evaluado una experiencia educativa que ha sido llevada a la práctica con niños y niñas de cinco años, en un centro caracterizado por una gran diversidad cultural y étnica. Se ha aplicado una metodología de investigación evaluativa, con enfoque cualitativo, empleando observación participativa, reuniones y entrevistas a las maestras que han implementado el proyecto. Los resultados de la experiencia muestran que dicha propuesta ha contribuido a promover el desarrollo emocional de los escolares y la cohesión grupal, a la par que se ha atendido a la diversidad. Asimismo, el profesorado ha actuado como guía, al escuchar y dar voz al alumnado. En conclusión, se ha constatado que el juego dramático ha resultado efectivo como estrategia promotora de una emocionalidad positiva en los escolares, y como estrategia de inclusión

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)0.013
metaresearch head score (Gemma)0.019
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 score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0040.003
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.462
Teacher spread0.422 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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