Equity, Diversity, and Inclusion in Post-Secondary Science Classrooms
Bibliographic record
Abstract
Canada’s post-secondary science courses have consisted of students with various backgrounds, views, cultures, and socio-economic statuses. These courses are instructed by professors with relevant qualifications and experiences. Due to everyone’s uniqueness, equity, diversity, and inclusion (EDI) are vital to consider within classrooms. This study details insight into the thoughts instructors have regarding EDI as it relates to teaching materials, assessments, and pedagogy. Semi-structured interviews were conducted, transcribed, and categorized into codes and analyzed. Results demonstrate there has been a shift in pedagogy from traditional lectures only to the inclusion of active learning activities, interactive problem-solving, discussions, and real-world scenarios. Participant instructors from the University of Windsor (UWindsor) report having taken courses that support pedagogical growth; however, they recommend additional courses about allyship and LGBTQ2S+ inclusivity. They also report that accessibility has been a prevalent theme as physical barriers are still visible in laboratories and classrooms. Additionally, being equitable towards students proved difficult in practice due to differences in student’s circumstances and backgrounds, as well as instructors’ ability to provide adequate accommodations. In all, UWindsor must provide instructors with continued support via courses and supplemental resources that offer practical means to facilitating students’ mental, physical, and emotional health and in turn promote EDI in science classrooms.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".