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Record W3155527147

The Effects of an Emotional Fairy Tale-Based Conceptual Class Model on Young Children's Linguistic Competence

2011· article· en· W3155527147 on OpenAlexvenueno aff
Hyun Seo

Bibliographic record

VenueEarly childhood education · 2011
Typearticle
Languageen
FieldComputer Science
TopicEducational Systems and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyVocabularyCompetence (human resources)ComprehensionDevelopmental psychologyLinguisticsMathematics educationSocial psychology
DOInot available

Abstract

fetched live from OpenAlex

The objective of this study was to examine the effects of an emotional fairy tale-based conceptual class model on young childrens linguistic competence and to provide basic information for developing effective teaching and learning methods. The subjects of this study were 40 young children in two five-year-old classes of H Kindergarten in G City, and they were divided at random to an experimental group (n=20) and to a control group (n=20) Aconceptualclassmodelwithactivitiestellingemotionalfairytaleswas applied to the experimental group, and the control group had activities telling ordinary fairy tales. The childrens linguistic competence was measured using the test sheet developed by Jang Yeong-ae (1981) and revised and supplemented by Park Ae-ja (1996). According to the results of this study, linguistic competence was significantly higher in the experimental group who experienced emotional fairy tales through the conceptual class model application. In addition, among the sub-factors of linguistic competence, vocabulary, verbal comprehension, and verbal expression were positively affected by the conceptual class model application in the experimental group. These results suggest that the application of an emotional fairy tale-based conceptual class model may be utilized as a new teaching-learning method for enhancing linguistic competence in the field of early childhood education.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.969
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.230
Teacher spread0.219 · 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 designObservational
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
Published2011
Admission routes1
Has abstractyes

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