SPEAR: Social Presence Enabled Augmented Reality Tool for Engineering Education
Bibliographic record
Abstract
This paper presents the development of a novel AR-based learning application named Social Presence-Enabled Augmented Reality (SPEAR) that presents opportunities for online peer learning through mobile devices. The app was developed using an AR Foundation framework in the Unity game engine. The learning module included in the app is structural beam-bending. The app allows users to place 3-dimensional (3D) virtual models of structural beams into the real-world environment. The users can then change the load and its position on the beam. A C# script for the finite element method was created to simulate the beam’s deformation based on the magnitude of load and load positions. In addition, the moment and shear diagrams based on the load and load position can be visualized in real time. Moreover, the voice chat feature was added to the application using a cloud-based server, Voice for Photon Unity Networking (PUN) 2, which delivers the feeling of social presence that is integral to online learning. This study demonstrates the technical feasibility of developing advanced visual and interactive learning materials for online engineering students in AR environments. As a prototype online-learning platform, the SPEAR app will allow researchers to test different learning theories and learning material designs generating new knowledge to improve online engineering learners’ motivation, self-confidence, and performance.
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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) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".