Inclusive Learning Through Equity-Driven Approaches to Design in Engineering Education
Bibliographic record
Abstract
Despite it being widely acknowledged that integrating principles of equity, diversity, inclusion, and accessibility (EDIA) in engineering has numerous benefits such as more innovative and inclusive design outcomes, there have been ongoing challenges in addressing equity, promoting diversity, and fostering inclusive teaching and learning strategies in higher education. If EDIA is taught separately from coursework in engineering, students are unlikely to engage with or incorporate EDIA principles in their work. Moreover, the pedagogical approaches for EDIA concepts, which include exploratory discussion and reflection, can be a barrier for student learning if they are not valued in the same way as traditional engineering epistemology and ideologies that prioritize a technical space and objective data. Lastly, evidence demonstrates that a lack of sense of belonging for underrepresented students can impact their learning experience. It is important to address how teaching approaches impact the shift of student mindsets in their design work and their engagement with the learning environment. In this exploratory project, we seek to understand the ways in which learning equity-driven approaches to design, such as design thinking, may impact engineering students’ perceptions of inclusivity in their learning environment and the quality of inclusivity in the work that they design and engineer. We focus on the rationale and development of the methodology in this paper since the work is still in progress. We are working with a diverse team of educators and researchers to review and revise course goals, student learning outcomes and related course content for two design thinking courses in a master’s level engineering program. Changes to the course material include further integrating EDIA principles and the “Liberatory Design framework” described by the Hasso Plattner Institute of Design as “a process and practice to liberate designers from habits that perpetuate inequity.” The impact of such revision is examined through qualitative analysis of students’ written reflections with prompts that focus on EDIA themes. A post-course survey is also used to assess students’ perceptions of EDIA in relation to their academic, professional, and personal learning experiences throughout the program and, more specifically, the design thinking course learning outcomes. Preliminary findings suggest that students connect design thinking approaches with an awareness of the value of diversity in their design teams and with more inclusive design outcomes for the end-user. This exploratory work can inspire research to further examine the role of design thinking and related pedagogical approaches in supporting the integration of EDIA principles in teaching engineering design.
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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 | 0.037 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".