THE IMPLICIT CANADIAN RESEARCH AGENDA FOR ENGINEERING EDUCATION: 2019
Bibliographic record
Abstract
Given the growth of the engineering education community in Canada, we argue that a research agendathat reflects our own identity and interests is needed. To start this conversation, we conducted a content analysis of the 2019 CEEA-ACEG conference proceedings to investigate the implicit Canadian research agenda for engineering education. We analyzed five characteristics: publications’ stream, level of collaboration, authors’ affiliations and, more importantly, their research topicsand areas. We found that the Canadian EER community is very practice-oriented, collaborative and that mostuniversities were represented at the conference. Also, seven main research areas were identified: Assessment,Teaching and Learning, Students, Faculty, Organizational, Engineering Education Discipline, and Philosophy of Engineering. Among these areas, Teaching and Learning is, by far, the one that received the most attention.
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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 | 0.108 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.033 | 0.011 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".