Lessons learned teaching during the COVID‐19 pandemic: Incorporating change for future large science courses
Bibliographic record
Abstract
Abstract Due to the COVID‐19 pandemic we were confronted with the transition of a large, on‐campus introductory soil science course into an online setting. This created several challenges, such as providing meaningful learning experiences to engage first‐ and second‐year students, and restructuring course content for the online environment. The objective of our article is to document the transition from on‐campus to online teaching and learning, through the consolidation of existing course material and the development of new resources to engage students in an introductory soil science course. We compare on‐campus, distance education, and online blended teaching and learning approaches for the same course, and provide lessons learned that may be applicable to other large introductory science courses. Our experience included the use of virtual laboratories, traditional online course materials, synchronous and asynchronous discussions, and the use of question banks for online exams. Recognizing the narrow preparation window, we focused on developing resources that provide benefits beyond COVID‐19.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".