Centres for Teaching and Learning Across Canada: What’s Going On?
Bibliographic record
Abstract
Post-secondary institutions, especially those with a research focus, face a challenge in ensuring consistent and high-quality teaching, in part because many members of the teaching faculty have backgrounds in research instead of teaching. A common part of meeting this challenge is the presence of Centres for Teaching and Learning (CTLs) on university campuses. This study examines the situations of CTL directors at research and teaching-intensive post-secondary institutions across Canada, with an aim to develop an understanding of the current context in which Canadian CTLs are operating, as well as the experiences of those who lead the CTLs. The qualitative study consisted primarily of 60- to 90-minute individual semi-structured interviews. The findings of these interviews were coded into four main categories: the evolving purpose of CTLs, key drivers of change, common challenges, and future trends. The implications for this research are two shifts: one mirroring a shift in education development to embracing Boyer’s work and secondly moving from a service to a leadership orientation. A thriving CTL is an indication of a university culture that values teaching, learning and scholarship. Les établissements d’enseignement post-secondaire, en particulier ceux dans lesquels l’accent est mis sur la recherche, doivent faire face au défi d’assurer un enseignement cohérent et de haute qualité, en partie du fait qu’un grand nombre de professeurs ont des antécédents en recherche plutôt qu’en enseignement. Pour relever ce défi, un des éléments communs est la présence de centres d’enseignement et d’apprentissage (CEA) sur les campus universitaires. Cette étude examine la situation des directeurs de ces centres dans des établissements d’enseignement post-secondaire canadiens où la recherche et l’enseignement sont intenses, dans le but de comprendre le contexte actuel dans lequel les CEA canadiens fonctionnent, ainsi que les expériences de ceux qui dirigent ces CEA. L’étude qualitative a consisté principalement d’entrevues individuelles semi-structurées de 60 à 90 minutes. Les résultats de ces entrevues ont été codés selon quatre catégories principales : l’évolution de l’objectif des CEA, les principaux moteurs de changement, les défis communs et les tendances futures. Les implications de cette recherche sont de deux sortes : la première reflète les variations dans le développement de l’éducation afin d’adopter le travail de Boyer, et la seconde indique le passage d’une orientation de service à une orientation de leadership. Un CEA florissant est le signe d’une culture universitaire qui accorde de l’importance à l’enseignement, à l’apprentissage et à l’avancement des connaissances.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.039 | 0.013 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".