Developing of Augmented Reality Media Containing Grebeg Pancasila for Character Learning in Elementary School
Bibliographic record
Abstract
Augmented reality media containing Grebeg Pancasila is the right tool for character learning in elementary schools. This research aims to (1) develop AR media containing Grebeg Pancasila for character learning in elementary schools, (2) find out its feasibility, and (3) test its effectiveness. The method used was the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. This research was conducted in elementary schools in Blitar City. The data were collected using literature study, observation, interviews, and questionnaires. The data were then analyzed qualitatively and quantitatively. The findings indicate that 3D AR media containing Grebeg Pancasila adds to the reality of the Grebeg Pancasila rite as the content of local wisdom in character learning. This media aims to provide complete knowledge about Grebeg Pancasila and support the learning of mutual cooperation. The media is attractive, portable, user-friendly, usable, and following the development of the 21st century. AR media containing Grebeg Pancasila was feasible for field testing through descriptive and inferential statistical tests. It has been proven that the media has a positive effect on the effectiveness of elementary school students' mutual cooperation character learning with a Sig 2-Tailed value of 0.00 (< 0.05).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".