Advancing Inclusivity in Engineering Education: Student perceptions of Design Thinking Courses and Workshops
Bibliographic record
Abstract
Integrating equity, diversity, inclusion, and accessibility (EDIA) principles into engineering education is crucial for addressing underrepresentation yet traditional disciplinary teaching methods hinder student engagement and learning. Building on previous research on engineering students’ perceptions of Design Thinking courses at McMaster University, this study investigates how Design Thinking courses and workshops shape student perceptions of inclusivity compared to traditional, non-design courses. Employing a mixed-methods approach, survey data were collected from 29 undergraduate and graduate students, and focus groups provided qualitative insights. The study examines perspectives across design and non-design courses, graduate and undergraduate learners, and equity-seeking versus non-equity-seeking groups. Findings reveal that design-focused pedagogies enhance comfort in expressing opinions, increase bias awareness, and improve the ability to develop equitable solutions, although they may also heighten sensitivity to bias. These results underscore the importance of student-centred, equity-based strategies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".