Incorporating Equity, Diversity, Inclusion and Intersectionality in First-Year Engineering: An Exploration of Students’ Application to Teamwork
Bibliographic record
Abstract
Teamwork skills are a vital learning outcome in higher education, yet negative interpersonal interactions within teams can diminish students’ sense of belonging and inclusion. To address this challenge, the introduction of equity, diversity, inclusion, and intersectionality (EDI&I) topics into course curriculums has been proposed. This study examines how integrating these concepts into a first-year engineering design course impacts students’ sense of inclusion and their ability to apply EDI&I principles to teamwork. Specifically, our research questions were: 1) To what extent did students experience a sense of belonging and uniqueness (inclusion) on their teams? 2) To what factors do students attribute their sense of belonging and uniqueness on their teams? And 3) How do students describe their design team experiences in relation to their ability to apply EDI&I? Data was collected through questionnaires from and interviews with forty-six participants using a qualitative empirical approach. Findings were that most students reported a strong sense of belonging and uniqueness within their teams, contributing to an overall feeling of inclusion. However, a small minority reported difficulties in applying EDI&I concepts to their teamwork. The discussion explores these difficulties and includes teaching strategies aimed at enhancing support for inclusive student teaming processes.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".