Turbulence in Crit Assessment: from the Design Workshop to Online Learning
Bibliographic record
Abstract
Critique in design education is redefining itself, but its primary aim still focuses on offering and receiving feedback on workshop projects. The global pandemic has forced teachers to adapt their methods for online workshops. The following paper questions how design critique has changed teaching and learning experiences, focusing on the distinctions between in-person and online sessions. Before winter 2020, students used to wander through the school’s workshops, filled with sketches and models of ongoing projects. Since then, we were faced with the loss of a shared physical space leading to many changes that should be addressed as online workshops are going forward. As a result, the pandemic has accentuated some of the challenges of offering detailed feedback to projects and has shown the complexity to stimulate students’ interactions during a critique. Gaps created through social distancing seem to have impacted not only the critique activity but the entire project and learning process. By exploring the teaching experiences of a dozen workshop tutors, this paper brings out concerns about the metamorphosis of general interactions and highlights an impact on the design activities. By referring to Lave and Wenger’s situated learning, we discuss the importance of interactions while conducting projects by explaining, discussing, showing, or just looking at what others have done. This paper provides an overview of key elements to improve feedback and communication, emphasising that constant interactions with peers, teachers, and experts are especially meaningful to prepare the designer to its future community of practice.
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.
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 | 0.019 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".