Collaborative Architectural Design Learning Experiment: using Second Life as a platform for design collaboration and review
Bibliographic record
Abstract
Design, in particular architectural design, is usually a collaborative activity. Indeed, design by collaboration has become a research paradigm in design studies. The focus of this paper is to present and discuss a collaborative architectural design learning experiement jointly undertaken by the Schools of Architecture of Toulouse in France, and Laval University in Québec City, Canada. Activities carried out by the pedagogical experiment FINC-AV (Forme, Information, Novation, Conception -Architecture Virtuelle) are held by two geographically distant studios that communicate and coordinate design information and tasks using the multi-user networked 3D virtual world of Second Life (SL). Innovation, as well as the dialogue about digital media, digital technology and design is at the forefront of FINC-AV experiment. For that purpose, SL is used as a platform for design collaboration (as a context as well as a collaborative and representational design tool) and reviews. Authors highlight the importance of giving emphasis to the communication of design solutions as well as the communication of the design processes in the evaluation of learning outcomes. They present descriptive results and discuss FINC-AV's main features. Proposals are made for future replications of this distant collaborative architectural design studio.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".