Development and application of competency mapping for equity, diversity, inclusion, and Indigeneity in out-of-curriculum learning opportunities for engineering students, staff, and faculty
Bibliographic record
Abstract
Equity, diversity, inclusion, and Indigeneity (EDI.I) is increasingly important in engineering. It applies to the institutional systems in which students learn, and also as learned content in the engineering context, which can be traced to both Canadian Engineering Accreditation Board (CEAB) Graduate Attribute 10 and the Truth and Reconciliation Commission’s Calls to Action. The objectives of this study were 1) to develop and evaluate a method for assessing comprehensiveness of EDI.I learning opportunities in engineering, and 2) to explore the comprehensiveness of out-of-curricular EDI.I workshops (for engineering students, staff, and faculty) using this method. We collected data via a survey to engineering EDI.I leaders at our institution. Learning opportunities were mapped to EDI and Indigeneity competency frameworks (categories: 1. Individual Relationship to EDI.I; 2. Interpersonal Impact of EDI.I; 3. EDI.I at an Organizational Level; and 4. EDI.I in Society) with Introduce, Develop, and Apply levels. Following mapping, we identified gaps. Most EDI.I content (seven workshops) focused on categories 1 and 2, teaching concepts closer to the individual (e.g. recognition of personal bias). There was minimal coverage of categories 3 and 4 (i.e. broader systemic inequities). Most EDI content was taught at the Introduce level, while the one Indigeneity workshop was focused on higher levels. The evaluation method was useful in identifying specific gaps in EDI.I learning outcomes. Limitations in this study primarily involved inadequate documentation in the collected data. We plan to apply this method and framework to EDI.I content within the undergraduate engineering curricula at our institution.
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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 | 0.049 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".