Teaching-Learning Process of Architecture Workshops in Virtual Environments Based on Research-Action Methodology
Bibliographic record
Abstract
In the School of Architecture at the Pontificia Universidad Católica del Ecuador, we are continuously reflecting on the teaching-learning process in order to offer the best education. The COVID-19 pandemic brings different changes in social, health, work and educative practices, which people have had to adapt to. These new conditions have shifted the perception of life and society, so it has demanded a new perspective to solve problems and meet the challenges that have arisen. It has happened with education, in which all stakeholders have been working to face and manage the educational practice in a virtual modality. Based on teaching experience, the present research is focused on the teaching-learning process in Architecture, considering design workshops during the first years of the major. The purpose of this paper, which uses an action research methodology, is to explore those changes that come about from this process in virtual environments. In this way, understanding architecture’s teaching and practice through virtual environments can generate an important impact that can transform the perspective on education in this field in the present and in the future.
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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 | 0.025 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".