Developing and deploying an introductory equity curriculum for engineering
Bibliographic record
Abstract
Equity, diversity, and inclusion (EDI) education is critical for engineering students, as the impact of inequity and colonization in engineering projects and processes can have long-lasting and widespread impacts. There are two challenges to equity in engineering practice: Canadian engineers as a group do not fully reflect the diversity of the community due to various systemic barriers, and they may not have been trained to consider whose perspectives are missing. We had the opportunity to embed EDI education within a larger second-year cohort program and link it explicitly to engineering.
 We created three EDI modules that were deployed in the 2020-2021 cohort. The format was video quizzes (introductory, asynchronous) and guest speakers with graded reflections (additional, more advanced content). The modules consisted of content concerning EDI in context, discussing bias, privilege, intersectionality, colonialism, race and specific racisms, gender, sexual orientation and discrimination in society with a special focus on links to engineering (including barriers engineering students may experience).
 We collected pre- and post-survey data. Most students agreed that they were familiar with most of the concepts already (71%), but most students also agreed that they learned a lot from the EDI modules (74%). We attribute this to lacking familiarity with applying EDI concepts in engineering contexts. Two thirds (68%) agreed the content would help in their professional lives. When asked an openended question about the most impactful thing they learned, just over half of the responses explicitly mentioned engineering, professional life, and/or workplaces. This indicates that our goal of tying EDI content to engineering and professional activities was successful.
 Overall, we successfully integrated an EDI curriculum into an existing second-year program, linking the content explicitly to engineering.
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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 | 0.000 |
| 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".