Design of Virtual Reality Zoos Through Internet of Things (IoT) for Student Learning about Wild Animals
Bibliographic record
Abstract
One of the functions of the Zoo is educational tourism. However, the obstacle when they are at the Zoo is that visitors rarely see animals moving freely and do not see the overall shape of the animal's body because some animals are dangerous and cannot be touched carelessly. In addition, the information presented on the information boards is minimal. With these problems, an idea emerged to create a system that could help as an educational medium, especially for school students, in an exciting way. This research aims to develop a Virtual Reality (VR) zoo with an Internet of Things (IoT) approach as an educational medium for recognizing wild animals. This VR is embedded in the YouTube application as a medium for running it so that students can use an Android-based smartphone; wild animal objects will appear in 3D animation and sound, along with information about wild animals. This research is development research using the Multimedia Development Life Cycle (MDLC) model. This application was tested on five smartphone users with the Android operating system. Based on the test results, the application system can run on several mobile devices using Android from Version 5.1.1 to Android 11. The IoT-based VR zoo application was successfully built to become an alternative for students and tourists who want to see and interact with wild animals up close. Future researchers are expected to be able to analyze this VR application on students' understanding of the concepts of the material being taught.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".