National Strategies for OER and MOOCs From 2010 to 2020
Bibliographic record
Abstract
A global agenda (Education 2030 Incheon Declaration and Framework for Action) published in September 2015 by UNESCO provides a roadmap for the next 15 years for education planners and practitioners. The main goal of the agenda is recognized as “ensuring inclusive and equitable quality education and promote lifelong learning opportunities for all”. The Member States develop policies and programs for the provision of quality for open and distance education with sustainable financial and legal framework and use of technology, including the Internet, open educational resources, massive open online courses (MOOCs) and other modalities to improve access in order to reach this goal by 2030. Institutions have realized the full potential of OER and MOOCs and started to develop their own policies with regard to teaching, learning and research resources in the public domain. In this regard, the purpose of this study is to examine national strategies on OER and MOOCs in the leading countries such as USA, UK, Canada, Japan, South Korea, and Turkey.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".