Augmented Reality Applications in Education: Arloopa Application Example
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".