THE GRADUATE SPECIALIZATION IN ENGINEERING EDUCATION
Bibliographic record
Abstract
There has been substantial growth in the formal focus on the pedagogy of engineering in the last two decades. Formalized pathways in Engineering Education (Eng.Ed), including Master’s and Ph.D. degree programs and university departments, have been established in several prestigious universities globally, with many founded in the U.S.. Interest in Eng.Ed in Canada has also grown, but up until very recently there has only been one formal pathway for graduate research in this field. In Fall 2020, the Department of Biosystems Engineering at the University of Manitoba welcomed the first three doctoral students into the Graduate Specialization in Eng.Ed (GSEE). In this paper we discuss the motivations for, and objectives and benefits of the GSEE, and describe its development. We share challenges encountered, and opportunities envisioned, and theintentions and motivations of the three graduate students who chose this pathway. We reflect on the importance of Eng.Ed programs for the advancement of engineering education research and the development of the discipline in Canada. Descriptions of our efforts and challenges areintended to help the development of additional Eng.Ed specializations or graduate programs in Canada.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.016 |
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".