Lessons learned teaching during the COVID‐19 pandemic: Incorporating change for future large science courses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it