Teaching-Learning Process of Architecture Workshops in Virtual Environments Based on Research-Action Methodology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it