Teaching Post-Secondary Students in Ecology and Evolution: Strategies for Early-Career Researchers
Bibliographic record
Abstract
Teaching can be a rewarding, yet challenging, experience for early career researchers (ECRs) in fields like ecology and evolution. Much of this challenge arises from the reality that ECRs in ecology and evolution typically receive little, if any, pedagogical training or advice on how to balance teaching, research (which can include extended field work), and other demands on their time. Here, we aim to provide accessible, pragmatic advice for ECRs in ecology and evolution who are given the opportunity to teach (as instructor of record). The advice is based on the authors’ collective experiences teaching in ecology and evolution as ECRs and is meant to help ECRs address two challenges: a) balancing the demands of teaching against one’s research, service, and personal life, and b) being effective in the classroom while doing so. The guidance we provide includes practical steps to take when teaching for the first time, including carefully refining the syllabus (course planning), adopting ‘non-traditional’ teaching methods, and taking advantage of free teaching resources. We also discuss a range of ‘soft skills’ to consider including guarding against imposter syndrome (i.e., self-doubt and fear of being exposed as a fraud), managing expectations, being empathetic, compassionate, authentic, and fostering an inclusive classroom. Lastly, we emphasize the need to focus on developing students’ critical thinking skills, integrating research and teaching where possible, and setting limits on class preparation time to maintain balance with your research and personal life. Collectively, we hope the examples provided herein offer a useful guide to ECRs new to teaching.
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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 | 0.019 | 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 | 0.010 | 0.004 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".