Religious Moderation Picture Book Innovation for Early Childhood: Teaching Tolerance and Respect for Diversity
Bibliographic record
Abstract
This study aims to develop innovative media in the form of picture books as a tool to teach religious moderation values such as tolerance and respect for differences to PAUD students in Purwakarta Regency. By using the Research and Development method based on the ADDIE model (Analyze Design Develop Implement Evaluate). this research is designed to produce educational media that is interesting, interactive, and effective. The initial stage of the research involved a needs analysis through interviews and observations with early childhood educators, which revealed the need for media capable of introducing the concepts of tolerance, respect for differences, and national values from an early age. The design and development process resulted in a picture book with vivid illustrations and stories relevant to children's daily lives, making it easy to understand and interesting. This media was tested in several early childhood education institutions in Purwakarta involving teachers and children as research subjects. The evaluation results showed that the picture storybooks improved children's understanding of the values of religious moderation. The results showed an average of the pre-test score of 22.08 then the Post-Test score reached 53.24, with an average increase of 31.16 with the calculation results showing the t value of -44.56, it can be concluded that the use of picture book media is effective in increasing religious moderation in children aged 5-6 years. With these findings, the developed media can serve as a strategic educational media.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".