Pivoting Open? Pandemic Pedagogy and the Search for Openness in the Viral Learning Environment
Bibliographic record
Abstract
This paper is based on the authors’ experiences and reflections working in educational technology and design support roles in higher education during the COVID-19 pandemic. We retrace our lived experience from the beginning of the pandemic in the spring (from our vantage points in the UK and Canada) and the associated ‘pivot online’ enacted in education around the world, through to the autumn of 2020, when we appeared to be transitioning into a so-called ‘new normal’ of the mid-pandemic. As digital education practitioners, who are also educators, researchers, and also simply as humans and friends living through a global pandemic, we had turned to each other initially for support in terms of work, wellness, and sharing news, information and sense-making, during which we began to consider researching under-examined dimensions of the evolving situation. The experiences and issues we reference are drawn from our own work, as well as from our responses to popular narratives advanced by key voices who have encouraged certain interpretations of the pandemic and its educational effects. Using Schön’s (1983) reflection-in-action lens, we examine these experiences and narratives of pandemic pedagogy through the frame of our multiple identities. In particular, from our perspective as researchers and advocates of open education, we noted calls for openness (such as the use of open educational resources) in response to the online pivot, which did not appear to be cutting through the noise of the sudden deluge of information, advice and broadly negative coverage of online teaching. However, through our reflective narrative and synthesis, we offer an alternative interpretation, which is that openness was nonetheless flourishing, but that the ‘pivot open’ was to practices rather than resources. Open exchange, community building and support amongst educators were apparent in multiple contexts. While pandemic profiteering has highlighted the need for open resources and infrastructures, and we anticipate this case continuing to be made more strongly as we emerge from the emergency, it is the turn to open practices which has met the immediate needs of educators and learners through community, interactions, sharing and care.
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.074 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.006 | 0.010 |
| 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".