Training graduate students and postdocs in course design: A workshop to bridge the gap between new professors’ pedagogical background and institutions’ teaching requirements
Bibliographic record
Abstract
Research-intensive institutions tend to reach hiring decisions mostly based on the applicants’ research achievements with minimal consideration of teaching experience and training. As a result, new professors often have little experience in teaching and lack education on pedagogy. However, graduate students and postdocs who plan to work in academia could be trained in advance to improve their early years’ teaching performance. The Tomlinson Project at University Level in Science Education (T-Pulse) at McGill has recently started offering the Teaching Techniques for Instructors Workshop for graduate students and postdocs. This is a one-day workshop that provides participants with the theoretical and practical tools to design and teach an effective university course. It has been designed to emphasize the alignment between learning outcomes, teaching strategies, and assessment tools; and participants are counselled on the importance, preparation and content of teaching statements used in the application process to teaching jobs. Surveys conducted throughout the workshop, showed that only 17% of attendants feel prepared to teach a university level course, and 63% think that the workshop will help them be more competitive when looking for teaching positions. Remarkably, 86% of the participants agreed that attending a workshop was a better option than taking a 3-credit course, and 100% of them preferred it to learning on their own. These results highlight the potential of the workshop in helping graduate students and post-docs transition into their academic careers, not only benefiting them but also the hiring institutions.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this metaresearch. It is in the settled core of the field.
Evaluates a workshop preparing graduate students and postdocs for academic careers, with survey data on their readiness and competitiveness for academic positions; the object is the training and careers of the research workforce.
The work evaluates training and career preparation for graduate students and postdocs as members of the research workforce.
Evaluates a workshop training graduate students and postdocs for academic teaching careers; research workforce training and capacity.
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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".