COMPARING ACADEMICS AND PRACTITIONERS Q & A TUTORING IN THE ENGINEERING DESIGN STUDIO
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it