The Effect of the Application of Feedback and Reflection on an Iterative Student Design Challenge
Bibliographic record
Abstract
In its fifth year, the Architectural Engineering Design Days challenge at the University of Waterloo (UW) returned to a fully in-person format in 2022.The event has evolved after being online in 2020 and hybrid online/ in-person in 2021.For the first time last year, instructors have integrated a second phase of the design challenge into a studio course.The two-phased version of the challenge has provided an opportunity for the authors to study the student work developed before instruction, and the influence of design critiques and feedback on the results of the second phase.The Design Days challenge for 2022 was for students in groups of 4 to design a piece of outdoor furniture for a given site on campus.Student teams were tasked with building a full-scale working mock-up of their design using limited supplies.At the end of a 48-hour design sprint early in the term, student teams presented their mock-ups to panels of professors and industry guests to receive feedback.One month later, the project was reintroduced to the same groups of students, but this time integrated into a design studio course.As part of the introduction to the second phase of the design challenge, the instructor presented a lecture on universal design.Students received two weeks of design development time, and two sessions of instructor and TA (Teaching Assistants) feedback to advance their design during the second phase.The final products of the second phase included updates to the original concepts in terms of design and construction, but also considerations of diversity in user experience.In this paper, the authors review the improvements made to the Design Days Challenge as it returns to an all in-person event.Also included is an overview of the perceived advances in project results from phase 1 to phase 2 from the course instructors.Most importantly, the results of a student survey will share the students' reflections on the modifications they made to their projects based on the receipt of feedback and course instruction during phase 2 of the Design Days challenge.
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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 | 0.013 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 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".