A Self-Directed Module To Empower Engineering Students To Engage with Equity, Diversity, Inclusion, and Indigeniety
Bibliographic record
Abstract
Background: Engineering faces significant EDI-related challenges. Despite the professional value of EDI being well established, related content in engineering curricula remains limited, and its valuation by students remains mixed. Purpose: Greater integration of EDI in engineering programs can increase engagement with, and valuation of, these concepts. A new student-driven EDI module deployed in both technical and non-technical courses was developed and assessed. Approach: Students engaged with self-selected EDI-related activity, and submitted reflections which were analyzed to assess their emotional, behavioural and cognitive engagement with the material, and level of EDI competence. Outcomes: Results suggest a notable increase in engagement and appreciation of EDI topics following the activity, suggesting that the module is effective at increasing the valuation of EDI in engineering. Conclusions: Given this flexibility and self-directed nature, the activity can be assigned multiple times. It may therefore be strategically integrated throughout the curriculum.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".