An Educational Augmented Reality Application for Elementary School Students Focusing on the Human Skeletal System
Bibliographic record
Abstract
Augmented Reality (AR) as a new field regarding Human Computing Interaction (HCI) has been gaining momentum in the last few years. Being able to project interactive graphics into real-life environments can be applied in various fields, research and commercial goals. In the field of education, textbooks are still considered to be the primary tool used by students to learn about new topics. Since AR requires interaction and exploration, it brings a ludic component that is hard to replicate using regular textbooks. The application we developed allows elementary school students to interact with a fully three-dimensional human skeleton model, using specialized virtual buttons. Students can understand this complex structure and learn the names of important bones just by using a tablet, a picture and their hands. Results show that the majority of students consider that our AR application helped them visualize and learn more about the human skeletal system. Additionally, the data we gathered shows that there was a 16% increase in correct responses regarding bone names after using our AR application. Our AR application successfully helped the students learn about the human skeletal system by introducing them to AR technologies.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".