Engineering Education at the University of Toronto
Bibliographic record
Abstract
Engineering education research at the University of Toronto is very active and growing. We currently have multiple faculty involved in individual and collaborative projects, and two Ph.D. candidates. Project topics areas include: teaching and assessment of teamwork and leadership; development of professional identity through portfolios; inverted classroom teaching methods; teaching effectively to diverse student populations; retention and grittiness; use of technology in the classroom and innovative pedagogy; outcomes based assessment methods; and collaborating on a study in critical thinking. We maintain an informal research group called PEER (Practitioners in Engineering Education Research) that meets approximately monthly. PEER group is a community of practice intended to help members develop research projects and proposals, provide additional perspectives on results, and discussion of shared interests. This network has been very effective in leveraging individual expertise and supporting a vibrant research community. Our Faculty is now taking the next step to establish a pathway for students pursuing a Ph.D. in engineering education. A task force to create a research based graduate program in engineering education was initiated in January and is currently developing a plan. In this session we will talk about the projects that are on-going and the plans for the future.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.245 | 0.039 |
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".