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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".