Prototyping a Spatial Skills AR Authoring Tool for Partially Sighted, Blind, and Sighted Individuals
Bibliographic record
Abstract
Spatial skills are critical for understanding the relations among objects and people, playing an important role in how we interact with the world. Spatial relationships are built through interactions with physical objects; however, in computational/online environments, these change to bi-dimensional media and computer-assisted design comprised of 3D representations viewable through a flat screen. Due to spatial immersion and interaction limitations, a traditional 2D and 3D approach presents challenges to partially sighted, blind, and sighted individuals. This paper presents the prototyping of a co-design Augmented Reality (AR) authoring tool by recruiting inclusive emerging affordances of consumer-level AR technologies within the context of current e-learning provisions in subject matters, including inclusive design, engineering design, game hardware design, and health sciences. This work has been inspired by the COVID-19 pandemic that has shown the need to level the field in inclusive design for teaching a subject typically oriented to the sighted. Our prototype allows users to create e-learning content for visualization, interaction, collaboration, and inclusive learning. Future work will investigate our tool's impact on skills development and content creation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".