Supporting teaching practice, program improvement, and accreditation efforts in an engineering program
Bibliographic record
Abstract
This paper emphasizes the essential role of a support person for faculty teaching and assessing the Canadian Engineering Accreditation Board (CEAB) graduate attributes as part of an ongoing accreditation cycle. It details the continuous program improvement process adopted by the Department of Biosystems Engineering at the University of Manitoba, and the role of engineering stakeholders. It recounts a study that details the supportive efforts of a Research Associate who helped to validate and implement rubrics with individual professors as outcomes-based tools for teaching and assessing the 12 CEAB graduate attributes, which resulted in the creation of 14 rubrics for 12 courses. Findings included new pedagogical understandings, the appreciation of individual support from the Research Associate, and the continued use of rubrics; the work led most professors to think deeply and in new ways about teaching and assessment. There was evidence that six professors engaged in ‘reverse design’, developing rubrics with targeted learning outcomes and course materials in mind. The work led to critical improvement in teaching practices and evidence of continual program improvement. Despite overall engagement and success, some professors continued to struggle with the concept and use of rubrics. In sum, this experience emphasizes the benefit of a dedicated person to support professors to implement rubrics, and in creating and sustaining an outcomes-based assessment culture in the department.
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 | 0.058 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".