Impact of COVID-19 on Formal Education: An International Review of Practices and Potentials of Open Education at a Distance
Bibliographic record
Abstract
In terms of scale, shock, and disenfranchisement, the disruption to formal education arising from COVID-19 has been unprecedented. Anecdotally, responses from teachers and educators around the world range from heightened caution to being inspired by distance education as the “new normal.” Of all the challenges, face-to-face and formal teaching have been most heavily affected. Despite some education systems demonstrating resilience, a major challenge is sustaining quality and inclusiveness in formal education suddenly delivered at a distance. In probing these issues, this article profiles international perspectives on the role of open education in responding to the impact on formal school and higher education caused by the COVID-19 pandemic. We proceed by highlighting and analysing practices and case studies from 13 countries representing all global regions, identifying and discussing the challenges and opportunities that have presented themselves. Reports cover the period from the beginning of 2020 until 11 March 2021, the first anniversary of the COVID-19 outbreak as declared by the World Health Organization. In our comparative study, we identify seven key aspects of which three (missing infrastructure and sharing OER, open education and access to OER, and urgent need for professional development and training for teachers) are directly related to open education at a distance. After comparing examples of existing practice, we make recommendations and offer insights into how open education strategies can lead to interventions that are effective and innovative—to improve formal education at a distance in schools and universities in the future.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".