Harnessing Open Educational Resources to the Challenges of Teacher Education in Sub-Saharan Africa
Bibliographic record
Abstract
The challenges to teacher educators in sub-Saharan Africa are acute. This paper describes how the Teacher Education in Sub-Saharan Africa (TESSA) consortium is working within institutional and national policy systems to support school-based teacher professional development. The TESSA consortium (13 African institutions and 5 international organisations delivering teacher education across 9 countries) designed and produced a bank of open educational resources (OERs) to guide teachers’ classroom practices in school-based teacher education. Drawing on examples from the TESSA consortium and from the University of Fort Hare, South Africa, the authors categorize the forms of TESSA OER integration as highly structured, loosely structured, or guided use. The paper concludes by outlining success factors for the integration of OERs: accessibility, adequate resources, support for teachers, accommodation of local cultural and institutional practices, and sustainable funding.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".