Impact of COVID-19 on Formal Education: An International Review of Practices and Potentials of Open Education at a Distance
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
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.
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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.004 |
| 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