Teaching Teaching to Undergraduates: A Case Study of an Independent Study in Music History Pedagogy
Bibliographic record
Abstract
Teachers at the post-secondary level are not taught to teach, except in rare cases in which graduate programs offer teaching training as part of their coursework. With no previous training, many students who go on to graduate school are often asked to teach undergraduates. For those who go on to teach in high school, middle school, and elementary school settings, education degrees prepare students for certain kinds of pedagogy, but miss out on the rich opportunities that are afforded by the university environment and its particular way of engaging with adult students. For those who go on to business, the arts, or science careers, the supervision of direct reports, junior colleagues, and employees is changing from a top-down authority-based relationship to more “teaching”—an exploring, developing and sharing relationship of peers. As well, students who are seeking graduate school acceptance need an arsenal of skills and competencies to compete for places, and training in teaching undergraduates strengthens the dossiers of these students. This paper outlines an independent study course in pedagogy that transformed both participants. Course objectives, assignments, feedback, and evaluation as well as caveats for those wanting to design a similar course, are described.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| 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".