The Foundation and Building Blocks of Inclusive and Equitable Classrooms in STEM
Bibliographic record
Abstract
Equity and Inclusion are matters of Justice, a laudable goal in its own right. They are further matters of Justice in science, technology, engineering, and mathematics (STEM) classrooms. There have been calls across the globe for more diversity in the workforce. Specifically in Canada and the United States of America, there is a need for diverse problem solvers to tackle the unique challenges societies face today. STEM disciplines provide individuals with the skills to help solve these problems, but are also disciplines that push out individuals from underrepresented groups, and therefore lack diversity. We see a great attrition of qualified individuals and diversity at the university level that perpetuates the lack of diversity in the workforce. One research area of discipline-based education research (DBER) assesses how equitable and inclusive classrooms can support and retain individuals from underrepresented groups in their fields. While the evidence of how to use equitable and inclusive classroom practices and tools is vast, these practices and tools are not fully accepted by the wider teaching community at the university level. We discuss what equity and inclusion are, the current state of STEM disciplines and the (lack of) progress toward equity and inclusion and the effect of retention of individuals from underrepresented groups. We provide a discussion on two main teaching practices in STEM classrooms, traditional lecturing and active learning, and where these practices uplift or fail students, especially those underrepresented in their discipline. These discussions aim to help the reader start their journey toward a better understanding of equity and inclusion in STEM 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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".