Enhancing quantitative skills in life sciences: Sustainable approaches to curriculum development
Bibliographic record
Abstract
Undergraduate life sciences students need to make sense of quantitative research and contribute to it. They must think critically about statistical issues in research and recognize statistical errors and weaknesses in research, which persist despite increased awareness and the tightening of journal reporting standards (Weissgerber et al., 2016). Developing quantitative skills is essential for life science students, as these skills are critical for understanding and solving complex biological problems. In a previous study, we found that while students recognize the importance of quantitative skills, they are underprepared and require more comprehensive training (White & Singh, 2023). However, our systematic review of quantitative requirements at U15 Canadian Research Institutions revealed that many life science programs require only one statistics course, if any (Tong et al., 2024). Subsequently, in 2023 we surveyed students in our introductory statistics course as well as upper-year courses in epidemiology, physiology, psychology, immunology and bioinformatics (n=382) to explore students’ perceptions about their quantitative preparation, and additional training that would benefit their development in their respective disciplines. Both survey studies were approved by the University of Toronto Social Sciences, Humanities, and Education Research Ethics Board. In this presentation, we will share results of the systematic review and latest student survey, discuss implications for quantitative training in life sciences and brainstorm sustainable ways to strengthen quantitative training in life sciences. By focusing on efficient and effective methods, such as integrating quantitative skills into existing courses and leveraging interdisciplinary collaborations, we aim to enhance quantitative training for our students while acknowledging the limited capacity for more courses given pressures on student course loads and faculty workloads. Please bring your own device (smartphone, laptop, tablet) so you can participate in our poll questions!
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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 | 0.103 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".