Evaluating Peer-led Feedback in Asynchronous Design Critiques: A Question-centered Approach
Bibliographic record
Abstract
Design critiques are a central component of the design studio.In engineering education, where the design studio pedagogy is becoming increasingly popular, peer-led critiques can play an important role to support and complement the feedback student teams receive from instructors and clients.In capstone design courses, peer critiques are typically delivered in face-to-face, synchronous environments, where students can demo their design progress and engage in constructive back-and-forth discussion with their peers.The disruption due to the COVID-19 pandemic, which has caused many design courses to be held remotely, has forced instructors to re-imagine how peer critique can be delivered in a virtual, mostly asynchronous setting.In this paper, we describe and evaluate an asynchronous and virtual implementation of peer critiques that are delivered using a text-based discussion forum.Taking a question-asking lens, we analyze hundreds of questions posed by students in asynchronous peer critiques of a capstone design course, and compare the distributions of low-level, deep reasoning, and generative design questions to results of prior studies that have produced analogous distributions in conventional face-to-face settings.We find that a larger portion of peer inquiry that is delivered in written form in asynchronous critiques is composed of generative design questions, which serve to expand the design space, and which have been previously found to be highly valued by design teams.Our findings serve to not only evaluate the effectiveness of the written, asynchronous approach to design critiques, but also support a discussion on how some of its features can be useful even when in-person peer design critiques are feasible.
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How this classification was reachedexpand
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".