Graduate student experiences of engineering education research in Canada
Bibliographic record
Abstract
In Canada, formal structures for advancing engineering education research (EER), including graduate programmes, funding, and career pathways, are uncommon. However, an active informal EER community exists. Using the Identity Trajectory Framework, we designed an exploratory basic interpretive qualitative study to learn how 21 graduate students experienced EER in Canada. EER’s dualist internal identity and lack of external identity, credibility, and structural support create a feedback loop of uncertainties for graduate students trying to navigate EER. This inhibits the healthy development of EER as an academic discipline, and thereby, the healthy development of EER graduate students. If we want to successfully support graduate students in developing their identity as EER researchers, we need institutional structures. Understanding how graduate students experience EER in Canada is important to understand how to build capacity here and in other locales where the field is newly developing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.032 | 0.015 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".