Design of Augmented Reality-based Interactive Learning Media to Recognize Endemic Animals in Early Childhood
Bibliographic record
Abstract
Education is currently experiencing a significant influence from the rapid development of technology. This encourages the world of education to continue to adapt, both in teaching methods and learning media. This adaptation is important so that learning remains relevant, interesting and effective for students, especially at the early childhood education level. This research aims to design learning media to introduce endemic animals in Indonesia by utilizing AR technology. This research uses the Design and Development (D&D) method, with the main focus being the process of creating or developing AR-based learning media carried out through the stages in the ADDIE model. The results showed that AR-based interactive learning media that had been designed, made and carried out validation tests. The validation results showed that the material expert gave a score of 30 with a good assessment category, while the media expert gave a score of 25 with the same assessment category. From these results, it is concluded that this media is declared feasible to use to introduce Indonesian endemic animals to early childhood. The implication of this research is that AR-based learning media can be an effective solution to introduce animals in a more real way, where the visual approach used is able to attract students' interest and increase their understanding, especially in recognizing local animals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".