Developing and deploying OERs in sub-Saharan Africa: Building on the present
Bibliographic record
Abstract
Open educational resources (OERs) have the potential to reduce costs, improve quality, and increase access to educational opportunities. OER development and deployment is one path that could contribute to achieving education for all. This article builds on existing information and communication technology (ICT) implementation plans in Africa and on the experiences of organizations and initiatives such as the African Virtual University (AVU), OER Africa, the South African Institute of Distance Education (SAIDE), and the Teacher Education in Sub-Saharan Africa (TESSA) Project, to present one view of the benefits, challenges, and steps that could be taken to realize the potential of OERs in sub-Saharan Africa. Thus, the article focuses on the factors necessary for creating and sustaining a vision for OER development and deployment; developing and distributing resources with an open license; improving technology infrastructure and reducing the cost of Internet access; establishing communities of educational collaborators; sustaining involvement in the OER initiative; producing resources in interoperable and open formats; establishing and maintaining the quality of OERs; providing local context to address national and regional needs and conditions; informing the public about OERs; and taking the initiative to build on the knowledge, skills, and experiences of others. In order to assist educators and decision makers, links to a variety of resources are provided.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".