Competency mapping for equity, diversity, inclusion, and indigeneity in engineering undergraduate curricula
Bibliographic record
Abstract
Educating students on equity, diversity, inclusion, and Indigeneity (EDI.I) in engineering education is motivated by both accreditation and Indigenous reconciliation. We aimed to evaluate EDI.I content comprehensiveness in our undergraduate curricula by applying a previously developed competency framework and method. We identified courses with EDI.I content across five programs, interviewed 14 instructors, and collected teaching materials. We mapped content to a competency framework with three mastery levels and categories of: Individual Relationship to EDI.I; Interpersonal Impact of EDI.I; EDI.I at an Organizational Level; and EDI.I in Society. Largest coverage gaps were at the highest mastery level and mid-program. Underlying messages often focused on individual rather than structural approaches. Most course content was either standalone or well-integrated with technical content. Our method identified gaps in EDI.I coverage, working well for standalone content. Results will inform future EDI.I curricular coverage at our institution, and the method allows progress assessment in EDI.I coverage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".