Augmented Reality Media Development in STEAM Learning in Elementary Schools
Bibliographic record
Abstract
The need for technology-based media is vital in the era of 21st-century education. Here, augmented reality media is a medium predicted to provide a more realistic experience successfully and in line with the developmental phase of elementary school students. For this reason, this study aims to analyze the need for augmented reality media development and develop an initial design for augmented reality media development in STEAM learning in elementary schools. This research used the R&D method with two of the four stages of R&D research. These stages are preliminary studies and initial design development. The subjects of this study were seven teachers and 129 students of a public elementary school in East Java, Indonesia. The instruments of this research were questionnaires and interviews. Data analysis used descriptive statistics and interpretive analysis. The results of this study revealed that teachers and students needed media that can represent material in-depth, increase interest and motivation, and provide experiences, such as STEAM-based augmented reality learning media. Meanwhile, the initial design development results of STEAM-based augmented reality learning media were in the form of six types of designs, containing Batik, Wayang (puppet), Gamelan, Borobudur Temple, Kereta Kencana (golden chariot), and Keris.
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".