Data-driven and instructor-engaged: Enhancing equity in Canadian STEM courses
Bibliographic record
Abstract
Panelists from the Canadian Consortium of Science Equity Scholars (CCSES)—a group of educators and researchers dedicated to enhancing equity in post-secondary science at the course level—will describe the approach and progress of the CCSES in collecting data and engaging instructors from across Canadian institutions. Small group discussions will allow the audience to engage with questions related to this large, collaborative, equity project and identify the opportunities and challenges of such a project. The CCSES builds on an emerging body of literature demonstrating the need to attend to affective dimensions of the classroom (Trujillo & Tanner, 2014; Eddy & Brownell, 2016) in creating an inclusive and equitable environment (Dewsbury & Brame, 2019; Theobald et al., 2020). The research goals of the CCSES include examining how the instructor-created classroom climate impacts students’ sense of belonging across science courses, institutions, and demographic groups, and identifying the inclusive teaching practices that help address inequities. In our work we seek to apply critical methodologies to reframe “achievement gaps” as “systemic and structural barriers” (Nissen, Her Many Horses, & Van Dusen, 2021). Since 2022, the CCSES has collected over 30,000 data points from tens of university STEM courses across 12 campuses. Each instructor receives a course report with summaries of the demographic makeup of their students and disaggregated information about student experience and outcomes. On the research side, our group is working on validating our measures and statistically modelling relationships. The project also includes qualitative sub-projects to help us understand the breadth of student experience. This research has been approved by ethics boards at all sites. References: Dewsbury, B., & Brame, C. J. (2019). Inclusive Teaching. CBE—Life Sciences Education, 18(2), fe2. https://doi.org/10.1187/cbe.19-01-0021 Eddy, S. L., & Brownell, S. E. (2016). Beneath the numbers: A review of gender disparities in undergraduate education across science, technology, engineering, and math disciplines. Physical Review Physics Education Research, 12(2), 020106. https://doi.org/10.1103/PhysRevPhysEducRes.12.020106 Nissen, J. M., Her Many Horses, I., & Dusen, B. Van. (2021). Investigating society ’ s educational debts due to racism and sexism in student attitudes about physics using quantitative critical race theory. Physical Review Physics Education Research, 17(1), 10116. https://doi.org/10.1103/PhysRevPhysEducRes.17.010116 Theobald, E. J., Hill, M. J., Tran, E., Agrawal, S., Nicole Arroyo, E., Behling, S., Chambwe, N., Cintrón, D. L., Cooper, J. D., Dunster, G., Grummer, J. A., Hennessey, K., Hsiao, J., Iranon, N., Jones, L., Jordt, H., Keller, M., Lacey, M. E., Littlefield, C. E., … Freeman, S. (2020). Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math. Proceedings of the National Academy of Sciences of the United States of America, 117(12), 6476–6483. https://doi.org/10.1073/pnas.1916903117 Trujillo, G., & Tanner, K. D. (2014). Considering the Role of Affect in Learning: Monitoring Students’ Self-Efficacy, Sense of Belonging, and Science Identity. CBE—Life Sciences Education, 13(1), 6–15. https://doi.org/10.1187/cbe.13-12-0241
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".