Augmented Reality Applications in Education: Arloopa Application Example
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
Arloopa is an augmented reality application that enables the integration of digital content such as images, sounds, texts into real world environments. By another definition the Arloopa app is an AR visualization tool that brings the physical and digital worlds together as one. Arloopa is an augmented reality (AR) and virtual reality (VR) app and game development company which provides advanced AR and VR services, such as: cloud-based augmented reality services, custom branded augmented reality app and game development, virtual reality app and game development, 2D and 3D content creation. In this study, the integration of Arloopa application into educational environments and application examples are presented within the scope of augmented reality applications course at a government university in Turkey. In addition, in this research, the presentation of the Arloopa application within a course unit and tips will be given to be used in future research on the integration of the application into education. At the end of the process, an interview form was prepared to determine opinions from the students about the Arloopa application and the use of augmented reality applications in education in general. The interview form prepared by the researcher was applied to 27 students within the scope of the course. According to the results obtained; the students found the use of augmented reality applications in education useful in terms of making the lesson fun, providing permanence in learning, and improving creativity skills. Despite all these positive aspects, the fact that some apps are salaried is accepted as the biggest limitation.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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