Adapting to Teaching During a Pandemic: Pedagogical Adjustments for the Next Semester of Teaching During COVID-19 and Future Online Learning
Bibliographic record
Abstract
COVID-19 has altered public health higher education and its impact on pedagogy will be felt long into the future. In response to social distancing measures, teaching academics implemented a number of changes to curricula. It is important to better understand and begin to evaluate these changes, as well as set a course for future changes to public health curricula both during and after the pandemic to best enable transformative learning. Teaching academics have an understanding of academic hierarchies and student perceptions and are well placed to provide insights into current and future changes to pedagogy in response to the pandemic. A survey was developed to examine changes that academics had made to their teaching in response to COVID-19. Responses were received from 63 public health teaching academics from five universities in Australia, the United States, and Canada. Public health teaching academics rapidly implemented a number of changes to their teaching, including alterations that enabled online teaching. The great majority of changes to teaching were related to tools or techniques, such as synchronous tutorials delivered in a video meeting room. There remains further work for the public health pedagogy community in reevaluating teaching aims and teaching philosophies in light of the COVID-19 pandemic. This could include examination of the weighting of different topics, including communicable diseases, in curricula. A series of questions to assist academics reformulating their curricula is provided. Public health teaching evolved rapidly to meet the challenges of COVID-19; however, ongoing adaptation is necessary to further enhance pedagogy.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".