COMPARING ACADEMICS AND PRACTITIONERS Q & A TUTORING IN THE ENGINEERING DESIGN STUDIO
Bibliographic record
Abstract
Abstract In the design studio, academic (professor) and practitioner tutors provide individual mentoring to students as they progress in their design projects. Prior studies suggest that design practitioners may follow a different design process compared to academics, but little is known about how this difference relates to their design tutoring. This study explores the similarities and differences in tutoring by academics and practitioners. We use a question-asking lens to characterize the tutoring styles of four tutors - two academics and two practitioners - over a five-week design project in an engineering design studio. We find that academic tutors ask questions at a significantly higher rate than practitioner tutors, suggesting a more question-centred tutoring style. We also find that proportionally more of practitioner tutors’ questions are generative in nature, while the academic tutors employ more convergent thinking in their questioning. This may be an indicator of the practitioners' own design thinking, which might be more solution-focused than that of academics. These preliminary findings motivate future investigations of the relationship between differences in tutoring and impact on student design learning.
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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 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".