The Effects of an Emotional Fairy Tale-Based Conceptual Class Model on Young Children's Linguistic Competence
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".