Applications of Extended Reality Technologies within Design Pedagogy: A Case Study in Architectural Science
Bibliographic record
Abstract
The concept of virtualizing and augmenting realities through technology has evolved from fantasy to feasibility and has advanced how humans are able to visualize and interact with the digital world.Extended realities (XR) often interpret threedimensional space in both realistic and conceptual forms, leveraging the ability of macro and micro scaling of computerized images.The versatility of VR is used in a wide range of disciplines from creative industries to professional practices and as an interactive multi-sensory visualization medium, it can be effectively adopted as a learning tool, used to elevate the experience in the classroom.This paper examines the possibilities of the incorporation of virtual reality, augmented reality, and mixed reality into the post-secondary architectural academic setting through lecture-based education, design pedagogy, project feedback delivery, and enhancement of experiential learning.The paper provides a case study of implementation into models of pedagogy at Canada's largest architecture program, in order to enhance the learning experience both within in-person and online learning contexts.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".