Exploring tutor-student interactions in a novel virtual design studio
Bibliographic record
Abstract
This exploratory case study investigates a novel adaptation of the virtual studio pedagogy. Students are located in authentic professional practice settings, while tutors remain on campus. Verbalisations of tutors' and students' discussions in 13 weekly sessions were characterized and measured using topic modelling and FBS analysis. Tutors and students exhibited large differences: Tutors' cognitive behaviour was generally more abstract while the students’ was more concrete. Further, while tutors were more concerned with design communication, students engaged with specific issues in the manufacturing and organizational setting of their host. Tutor-student interactions did not change significantly over time, with most of the interactions taking place in the solution space. Categorical differences in topics between tutors and students were found that remained consistent over time. • A novel virtual design studio pedagogy was created. • Students were situated in a practice setting, while tutors remained on campus. • Tutor-student interactions were characterized using topic analysis and FBS. • Categorical differences in topics during interactions remained consistent over time. • Tutors' cognitive behaviour was found to be more abstract than the students'.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".