The Continuing Education of Faculty as Teachers at a Mid-sized Ontario University
Bibliographic record
Abstract
The findings outlined in this paper are the result of focus groups conducted with faculty at a mid-sized Ontario university. These nine faculty, all of whom have received awards of excellence from their university for their teaching, shared their insights about how they developed as teachers over time. More specific topics explored were as follows: how they first learned about teaching; how they continue to learn about teaching; resources that might have helped early in their teaching careers at the university; and advice they have about teaching for new university teachers, mid-career teachers, and teachers approaching retirement. While many of the observations offered here are specific to Ontario and some of the literature review is North American in focus, the paper offers valuable insights into how faculty learn to be teachers which may be helpful to universities around the world. Cet article présente les résultats d’entrevues menées avec des groupes de discussion composés de membres du corps professoral d’une université ontarienne de taille moyenne. Les 9 professeurs participant ont tous reçu des prix d’excellence de leur université pour leur enseignement. Lors de ces rencontres, ils ont expliqué comment ils ont évolué à titre d’enseignants au fil du temps. Les sujets particuliers suivants ont été abordés : leurs premiers apprentissages en matière d’enseignement; leurs apprentissages subséquents; les ressources qui les ont aidés tôt dans leur carrière d’enseignant à l’université; les conseils qu’ils ont à offrir aux enseignants universitaires qui viennent de débuter leur carrière, à ceux qui sont à mi-parcours et à ceux qui approchent de la retraite. L’article fournit un aperçu utile sur la façon dont les membres du corps enseignant apprennent à devenir des enseignants. Même si bon nombre des observations présentées sont spécifiques à l’Ontario et si une partie de la recension des écrits est d’origine nord-américaine, ces informations peuvent servir aux universités à l’échelle internationale.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".