Global collaboration for sustainable biology education: Enhancing curriculum design in Canada
Bibliographic record
Abstract
According to the New York Times Higher Education report, in 2024, approximately 40% of Canadian universities declined in their academic rankings for teaching biological sciences on a global scale. Regional differences in higher education significantly impact student success, lifelong learning, and sustainability of the bioscience industry worldwide. Our ranking decline is of grave concern to multiple stakeholders involved with education: students, instructors, and employers, and underscores the urgency for improvements in biology teaching and learning. This research analyzes global undergraduate biology education by examining Program Learning Outcomes (PLOs) and program structures in top-ranked Bachelor of Science (BSc.) biology programs. By identifying key trends and best practices in science education, this presentation offers insights to enhance Canadian curricula and bridge educational gaps. Attendees will explore international strategies, engage in discussions with fellow educators, and leave with actionable implementation plans. Preliminary analysis reveals that Canadian institutions emphasize skill development but exhibit gaps in knowledge expansion and societal initiatives, limiting graduates’ ability to contribute as significantly to sustainable scientific communities after graduation. To address these gaps, this presentation will help attendees incorporate global best practices in the revision of their own programs, building off a Curriculum Update Checklist. This session will help promote sustainability through continuous global collaboration beyond this presentation. This work will demonstrate how globally informed strategies can be integrated into Canadian academia, equipping faculty with actionable ideas and tangible resources to enhance their teaching practices while ensuring the sustainability of higher biology education in professional settings. The approaches discussed in this presentation also apply across non-biology disciplines that value innovation, adaptability, and global collaboration. Students and educators can adopt these strategies by promoting interdisciplinary learning, global initiatives, exchange programs, and collaborating on community-based sustainability projects. Participants are encouraged to bring their own device (smartphone, laptop, or tablet) to engage with interactive tools and activities during this presentation! The Curriculum Update Checklist is in collaboration with MSc student Sidney Evans.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".