An Educational Augmented Reality Application for Elementary School Students Focusing on the Human Skeletal System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it