Engineering Education Research in Canada: Out from the Shadow of a Giant
Bibliographic record
Abstract
Background: Engineering education research (EER) is relatively new in Canada, and there is a sense it is “lagging behind” EER in the United States (U.S.). This relative newness, coupled with the epistemological and normative differences between its parent disciplines (engineering and education), presents many challenges to developing the EER community, including establishing physical and disciplinary homes, dedicated funding, and identity. These challenges impact how the field matures. Given the different phases of development between the two nations and their geographical and cultural proximity, EER scholars in Canada should be able to learn from the development of EER in the U.S. to address these challenges and advance the field in Canada. Purpose: This article offers a comparative look at EER in Canada and the U.S., so Canadian scholars and scholars living in areas where EER is emerging can learn from the development of EER in the U.S. and address the challenges in advancing the field in their region. Scope: We share our understandings of the contextual elements of EER in Canada compared to the U.S. based on the literature and on the scholarship and lived experiences of the authors. We theorize Canada’s challenges in developing the EER field using McAlpine’s (2012) Identity-Trajectory framework. Discussion/Conclusions: EER in Canada is challenged by a lack of home, funding, and identity. These tensions create a circular logic, preventing the growth of EER as a legitimate field in Canada, and must be addressed. The development of EER in Canada lags, yet follows similar patterns to EER in the U.S. As such, Canada should be able to advance EER in the Canadian context by learning from the national and international infrastructure that the U.S. has helped establish and following pathways that better suit the Canadian context.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.061 | 0.036 |
| Scholarly communication | 0.033 | 0.009 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".