Open Educational Resources: Policy, Costs and Transformation
Bibliographic record
Abstract
Open Educational Resources (OER) — teaching, learning and research materials that their owners make free for others to use, revise and share — offer a powerful means of expanding the reach and effectiveness of worldwide education. The Commonwealth of Learning (COL) and UNESCO co-organised the World OER Congress in 2012 in Paris. That Congress resulted in the OER Paris Declaration: a statement that urged governments around the world to release, as OER, all teaching, learning and research materials developed with public funds. This book, drawing on 15 case studies contributed by 29 OER researchers and policy-makers from 15 countries across six continents, examines the implementation of the pivotal declaration through the thematic lenses of policy, costs and transformation. The case studies provide a detailed picture of OER policies and initiatives as they are unfolding in different country contexts and adopting a range of approaches, from bottom-up to top-down. The book illuminates the impacts of OER on the costs of producing, distributing and providing access to learning materials, and shows the way that OER can transform the teaching and learning methodology mindset. Recommendations on key actions to be taken by policy-makers, practitioners, OER developers and users are also outlined, particularly within the context of Education 2030. Clearly, progress is being made, although more work must be done if the international community is to realise the full potential of OER. Contents Foreword by the President and CEO, Commonwealth of Learning Foreword by the Assistant Director-General for Education, UNESCO Introduction Open Educational Resources: Policy, Costs and Transformation | Rory McGreal, Fengchun Miao and Sanjaya Mishra Chapter 1 Open Educational Practices in Australia | Carina Bossu Chapter 2 Open Educational Resources Policy for Developing a Knowledge-Based Economy in the Kingdom of Bahrain | Nawal Ebrahim Al Khater, Hala Amer and Fadheela Tallaq Chapter 3 The State of Open Educational Resources in Brazil: Policies and Realities | Carolina Rossini and Oona Castro Chapter 4 Open Educational Resources in Canada | Rory McGreal, Terry Anderson and Dianne Conrad Chapter 5 Caribbean Open Textbooks Initiative | Neil Butcher, Andrew Moore and Sarah Hoosen Chapter 6 Open Educational Resources in Germany | Ulf-Daniel Ehlers Chapter 7 Copyrights in OER Publishing in India: The Case of the National Programme on Technology-Enhanced Learning | Mangala Sunder Krishnan iv Chapter 8 The Promise of Open Educational Resources in Indonesia | Petra Wiyakti Bodrogini and Mohammad Rinaldi Chapter 9 Using Open Educational Resources for Undergraduate Programme Development at Wawasan Open University | Teik Kooi Liew Chapter 10 OERu: Realising Sustainable Education Futures | Wayne Mackintosh Chapter 11 Integrating ICT for Innovative Educational Solutions in Oman: Leveraging OER Policy to Enhance Teaching and Learning | Maimoona Al Abri and Saif Hamed Hilal Al Busaidi Chapter 12 The Polish Open e-Textbooks Project as a Policy Model for Openness of Public Educational Resources | Alek Tarkowski Chapter 13 Open Access to Educational Resources Through Federal Portals and OER in Russia | Svetlana Knyazeva and Aleksei Sigalov Chapter 14 Open Educational Resources for Early Literacy in Africa: The Role of the African Storybook Initiative | Tessa Welch and Jennifer Glennie Chapter 15 Developing an Infrastructure Support for Faculty Use of Open Educational Resources: The Case of the Washington State Community and Technical Colleges System | Boyoung Chae and Mark Jenkins Conclusions | Fengchun Miao, Sanjaya Mishra and Rory McGreal
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.055 | 0.055 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.016 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".