DISCIPLINE-BASED EDUCATION RESEARCH (DBER) – WHAT IS IT, AND WHY SHOULD ENGINEERING EDUCATION RESEARCH SCHOLARS BE TALKING ABOUT IT MORE?
Bibliographic record
Abstract
Engineering Education Research (EER) is a growing field in Canada. However the interdisciplinary nature of our field means we frequently face challenges, often in the form of deficits: funding, support from colleagues, sufficient interdisciplinary expertise, and recruitment of and support for post-graduate students. In order to continue growing the EER field, we need to provide academic scholars and students with the necessary interdisciplinary support systems. This paper provides an overview of the field of Discipline-Based Education Research (DBER) and makes recommendations on how EER scholars can more intentionally engage. Specifically, through knowledge sharing, communities of practices, and collaborative infrastructures and systems.
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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 | 0.115 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.023 | 0.068 |
| Scholarly communication | 0.049 | 0.029 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".