The Digital Design Build - Modes of Experiential Learning in the Pandemic Era
Bibliographic record
Abstract
In recent years, academia has deviated from the lecture-based model to a hybridized system of instruction and experiential learning. Experiential learning aids students in understanding collaborative processes in architectural praxis and exposes them to engaging learning opportunities, a critical component of architectural studio education (Nijholt et al. 2013). During the COVID-19 outbreak, students are barred from accessing on-campus facilities. This causes a redevelopment of curricular delivery and disrupts experiential learning which heavily relies on in-person interaction. It is imperative for instructors to retain experiential learning in the transition to virtual instruction. This paper explores experiential learning within virtual platforms for instruction. Through outlining the implementation of technologies, capitalizing on connectivity, and maximizing opportunity for digital problem solving, the authors posit a framework that other educators may adopt. The paper concludes with a case study of a virtual design-build project, and the various techniques implemented in retaining experiential learning during the pandemic.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".