From Open Educational Resources to Open Educational Practices
Bibliographic record
Abstract
During the past twenty years Open Educational Resources (OER) and Open Educational Practices (OEP) have increased educational access and affordability worldwide. The OER-OEP synergy reflects one of the most promising strategies for increasing access to education globally. Despite significant progress, emerging research suggests the need for a more concerted focus on moving from awareness raising to implementation. This article analyses the current status of OER and OEP in concert with the recent educational response to COVID-19 by educational providers with complimentary mini-case studies from Africa, Brazil, Canada, China, Sweden, and Turkey. The ICDE Ambassadors for the global advocacy of OER, and members of ICDE OER Advocacy Committee (authors) argue that in the six countries studied, there is a greater need and greater receptivity to expand access to education through OER and OEP. To better address the immediate needs of the COVID-19 educational crisis, and to make longer term educational improvements, countries should harness the policy supports and actionable steps offered by the UNESCO OER Recommendation. Currently, we see an opportunity to move OER-OEP to resilient sustainable education and the international policy framework to support such work. The article concludes with some general observations for the way forward.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.053 |
| Scholarly communication | 0.021 | 0.031 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 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".