Teaching animal behavior online: A primer for the pandemic and beyond
Bibliographic record
Abstract
Behavior courses face numerous challenges when moving to an online environment, as has been made necessary by the COVID-19 pandemic. These challenges occur largely because behavior courses, like most organismal biology courses, often stress experiential learning through laboratories that involve live animals, as well as a lecture component that emphasizes formative assessment, discussion, and critical thinking. Although online behavior courses may be remote, they can still be interactive and social, and designed with inclusive pedagogy. Here, we discuss some of the key decisions that instructors should consider, provide recommendations, and point out new opportunities for student learning that stem directly from the move to online instruction. Specific topics include challenges related to generating an inclusive and engaging online learning environment, synchronous versus asynchronous formats, assignments that enhance student learning, testing format and execution, grade schemes, design of laboratory experiences including opportunities for community science, design of synthetic student projects, and workload balance for students and instructors. We designed this primer both for animal behavior instructors who need to quickly transition to online teaching in the midst of a pandemic, and for those facing such transitions in upcoming terms. Much of the manuscript's content should also be of general interest and value to instructors from all areas of organismal biology who are attempting to quickly transition to online teaching.
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 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".