Development Of Augmented Reality-Based Science Learning Media for Earthquake Mitigation Readiness at Qatrinnada Kindergarten in Padang City
Bibliographic record
Abstract
This study was motivated by the low level of knowledge and skills among early childhood students in earthquake mitigation, partly due to uninteresting learning media and a low level of integration of the latest technology. The aim of this study was to develop augmented reality-based science learning media to improve earthquake mitigation preparedness among early childhood students. The research employs the R&D Gall, Gall, and Ball development model with nine stages, involving validation by three experts (content, media, and instruments) and a pilot test with participants from Group B of Qatrinnada Kindergarten in Padang City. Data were collected through observation, questionnaires, and documentation, then analyzed using validity, practicality, effectiveness, N-gain, and paired sample t-test analyses. Validation results showed high validity for the material (89.23%) and instruments (86%), and validity for the media (76%). The media was deemed highly practical (92.30%) and provided knowledge improvement with moderate N-gain values for the small class (0.69) and large class (0.58). The t-test revealed a significant difference between pretest and posttest scores (sig. 0.000 < 0.05). Therefore, the augmented reality-based science learning media developed is effective in enhancing earthquake mitigation readiness among young children.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".