Teaching in Times of Crisis or Pandemic Pedagogy
Bibliographic record
Abstract
Abstract Higher education institutions (HEIs), including universities, adult and vocational institutes, and technical and further education (TAFE) centres, faced the challenge of responding to the COVID-19 pandemic with limited data on how best to protect their communities and to continue educating their students. HEIs implemented various measures and adaptations by prioritizing the safety and well-being of students, staff, and the broader community while ensuring uninterrupted educational delivery. The pandemic presented a global educational challenge, requiring institutions to address complex organizational issues. These challenges encompassed topics such as information access, equity, diverse communication infrastructures, collaboration, logistics, the use of digital platforms, decentralization, redundancy, variation in virtual rituals and communication protocols, unstructured digital proxemics, Zoom fatigue, the absence of remote feedback loop models, and COVID-19 management protocols. Among the critical questions posed by the pandemic in the higher education sector in Australia and Canada, whether at universities, technical institutes, or education centres, was how faculty enhanced the learning experience and fostered symbiosis among co-located/on-shore and remote/off-shore students. To gain a deeper understanding of the relationship between HEIs and COVID-19 educational mitigation, we analysed the actions taken by three HEIs in Australia and one in Canada during the crisis years of 2021–2022. This analysis was based on the personal reflections of the authors (academics from various HEIs), a synthesis of which is presented in this chapter.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".