Exploring the Effect of Immersive VR on Student-Tutor Communication in Architecture Design Crits
Bibliographic record
Abstract
Using digital tools like immersive Virtual Reality (iVR) reduce the carbon footprint by providing collocated and remote communication through virtual design studios. By providing a sense of presence in a digital display, iVR systems impact student-tutor communication during design critiques or crits. Research lacks studies articulating how iVRs change crits' communication to increase the ability to integrate iVRs as educational media and promote a quality education in inter-university studios. To this end, this study explores the cognitive structure of student-tutor communication during collocated architecture crits using iVR and non-immersive media. We employed protocol analysis to analyze divergent thinking by tracking the distribution of First Occurrences of design issues. Combining protocol analysis with Natural Language Processing, we explored the size of the design space generated during the crits. Results from a case study that includes twelve crits from three students show an increase in students‚ exploration of the design space and divergent thinking in the iVR crits, providing evidence that iVR enhances learners' communication. iVRs can be integrated to support remote design studios without the generation of carbon due to physical travel.
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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 | 0.018 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".