Impacting the teaching culture: Role of the department and the software tools
Bibliographic record
Abstract
Establishing a culture of teaching excellence among faculty with limited prior instructional training raises both practical and philosophical challenges. This paper argues that the departmental unit plays a critical role in setting the conditions necessary for faculty engagement, and that multiple strategies can be coordinated to target change in the teaching norms. The paper introduces the Integrated Course Design andDocumentation (ICDD) project at the Department of Mechanical Engineering, York University. The ICDD project demonstrates several of our approaches to effecting change in individual behaviour towards studentcentered pedagogy. They include: (1) making the solution easy for the faculty; (2) making the solution a stand-alone resource that the faculty themselves can develop over time; (3) speaking the language of the faculty (relevance, contextualization); and (4) providing the social, organizational, and practical support for faculty to make the transition. Overall, we argue that any effort to create and sustain change must be multi-faceted, and must include: enabling the instructors as the key agents of change; promoting collaboration among faculty; lowering practical barriers to change by developing technical, administrative, and educational resources that are fit to the local context.
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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.023 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".