Discipline-based educational development: examples from four Canadian universities
Bibliographic record
Abstract
"Discipline-based educational development", integrating the principles of teaching and learning with specific content knowledge of a discipline, is emerging as a complement to more traditional, centralized models of teaching support, bringing with it its own advantages and challenges. Partly, it is a question of belonging: it helps to be part of a team of people - possibly with a variety of specialties in areas like curriculum, pedagogy, educational technology - and operating from a centre offers this important support, but coming from a single unit across campus may make it harder to connect with those teaching in departments. Conversely, working in a department creates many opportunities to connect with faculty and students, but can be isolating as there is unlikely to be a team of any size at the department level doing similar work.\nThis panel discussion will explore four examples of discipline-based educational development at Canadian universities, highlighting successful initiatives and challenges faced by educators in implementing this approach. In one case, teaching is transforming via graduate student projects within specific courses, and the others have variations on teaching centre models with different levels of connections to departments - in one case with staff members embedded in departments. We will also be interested to learn of other models from those who attend the discussion. Overall, this panel discussion aims to raise awareness of the value of discipline-based education development in STEM education and to provide a platform for dialogue and collaboration among educators and educational developers in Canadian post-secondary institutions.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.034 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".