How the ecology and evolution of the COVID‐19 pandemic changed learning
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic introduced an abrupt change in human behavior globally. Here, we discuss unique insights the pandemic has provided into the eco-evolutionary role of pathogens in ecosystems and present data that indicates the pandemic may have fundamentally changed our learning choices. COVID-19 has indirectly affected many organisms and processes by changing the behavior of humans to avoid being infected. The pandemic also changed our learning behavior by affecting the relative importance of information and forcing teaching and learning into a framework that accommodates human behavioral measures to avoid disease transmission. Not only are these indirect effects on the environment occurring through a unique mechanistic pathway in ecology, the pandemic along with its effects on us provides a profound example of the role risk can play in the transmission of information between the at risk. Ultimately, these changes in our learning behavior led to this special issue "Taking learning online in Ecology and Evolution." The special issue was a call to the community to take learning in new directions, including online and distributed experiences. The topics examined include a significant component of DIY ecology and evolution that is experiential but done individually, opportunities to use online tools and apps to be more inclusive, student-focused strategies for teaching online, how to reinvent conferences, strategies to retain experiential learning safely, emerging forms of teaching such as citizen science, apps and podcasting, and ideas on how to accommodate ever changing constraints in the college classroom, to name a few. The collective consensus in our fields is that these times are challenging but we can continue to improve and innovate on existing developments, and more broadly and importantly, this situation may provide an opportunity to reset some of the existing practices that fail to promote an effective and inclusive learning environment.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".