Fostering Inclusive Learning in Engineering through Equity-Based Design Education
Bibliographic record
Abstract
The integration of equity, diversity, inclusion, and accessibility (EDIA) in engineering education is often overshadowed by traditional engineering ideologies that prioritize technical work and objectivity. Our research aimed to understand how learning equity-based design affected students' perceptions of inclusivity in their learning environment and the quality of their design work. The Design Thinking curriculum was enhanced to integrate EDIA principles and the Liberatory Design framework. We hypothesized that these design courses would positively impact students' equity-based design competencies and sense of belonging in engineering. Data were collected via post-course surveys and reflection assignments. Survey results from six participants indicated positive impacts on students' perceptions of inclusivity and agency for social change. Thematic analysis of reflection assignments revealed a link between self-awareness, inclusive design, and empowerment to challenge societal norms. Despite the study's exploratory nature and small sample size, findings suggested that engaging in equity-based design approaches fostered inclusive learning in engineering education.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".