OER Mainstreaming in Cameroon: Perceptions and Barriers
Bibliographic record
Abstract
The government of Cameroon has been increasingly pre-occupied with the quality of learning outcomes and the lack of learning resources at all levels of the education system. Research on similar educational systems in Sub-Saharan Africa and beyond indicate that Ministries of Education are exploring the potential of open educational resources (OER) to cut down the high cost of textbooks and enhance the availability of quality learning materials in classrooms. To explore possibilities of mainstreaming OER under the Ministries of Basic and Secondary Education in Cameroon, a quantitative research design approach was used to survey n=393 Regional Pedagogic Supervisors from the 10 Regions of the country. The outcome of this study presents the factors shaping the perspectives of Regional Pedagogic Supervisors in terms of perceptions and barriers to using OER. The novelty of this approach is the application of a proven model for technology acceptance testing in the context of OER. Based on the findings, three major recommendations for mainstreaming OER in Cameroon with potential impact on lowering textbook costs and increasing learning outcomes were formulated.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".