Benefits and Drawbacks of Using Multiple Instructors to Teach Single Courses
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
We set out to identify the benefits and drawbacks of using more than one instructor to teach single section science courses at a large research university. Nine courses were investigated involving widely differing subjects and levels. Teaching models included: sequential teaching with two to six instructors each covering only their own modules, two teachers present in class at all times, and hybrids of these two models. A three-question survey was answered by 957 students and 17 instructors. Dominant advantages identified by both groups were variety of teaching style or perspectives and instructor expertise, with instructors being more likely to identify expertise as the primary advantage. Dominant disadvantages identified were adjustment to teaching style and expectations and confusion and communication issues. Data suggest that advantages are maximized and disadvantages minimized either in courses with two or more instructors interacting and collaborating in class or when special care is taken with coordination and collaboration if the course is sequentially taught. We conclude with specific recommendations to instructors and departments based on evidence from the data.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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