The Influence of Animation Videos on Expressive Language Skills in Preschool Kobar Anugerah, Batujala Village, Bontoramba District, Jeneponto Regency
Bibliographic record
Abstract
This research aims to: 1) find out what the effect is after being given an animated video at the Kobar Anugrah Early Childhood Education in Batujala Village. 2) To find out what the influence was before being given an animated video at the Kobar Anugrah Early Childhood Education in Batujal Village. 3) To find out whether there is an influence of learning videos in Kobar Anugrah Early Childhood Education in Batujala Village. This research is a quantitative descriptive research by describing variables that support the data in the form of numbers generated from the actual situation. This research uses primary data which collects data using questionnaires and interviews. The researcher uses quantitative analysis, multiple linear regression analysis and analysis of the coefficient of determination. with the help of SPSS17. The population in this study was 47 early childhood children at the Kobar Anugrah Early Childhood Education School, Batujala Village, carried out on May 15 2023 at Kobar early childhood education in Batujala Village, Bontoramba District, Jeneponto Regency, South Sulawesi Province. The results of the research obtained using the Animated videos have the effect of helping young children be active in expressive language at early childhood education schools in Kobar, and teachers are also more active in the early childhood education learning process using animated digital media learning methods. And master digital media materials with animated video materials.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".