Challenges of Adopting Open Educational Resources (OER) in Kenyan Secondary Schools: The Case of Open Resources for English Language Teaching (ORELT)
Bibliographic record
Abstract
Kenya, like many African countries, has faced enormous challenges in the production of and access to quality relevant teaching and learning materials and resources in her primary and secondary school classrooms. This has been occasioned by a plethora of factors which include, but are not limited to a lack of finances, tradition, competence, and experience to develop such resources. Such a situation has persisted despite the existence and availability of many Open Educational Resources (OERs) that have been developed by many education stakeholders at enormous costs. Such freely available resources could potentially improve the quality of existing resources or help to develop new courses. Yet, their uptake and reuse in secondary and primary schools in Kenya continues to be very low. This paper reports the findings of a study in which Open Resources for English Language Teaching (ORELT) developed by the Commonwealth of Learning (COL), Canada, were piloted in sampled fifty (50) Kenyan secondary schools. The study applied the Model 1 – Distance and Dependence (Zhao et al 2002) model to investigate the challenges that hinder instructors to adopt and use ORELT materials. The study reported that poor infrastructure, negative attitudes, lack of ICT competencies, and other skill gaps among teachers and lack of administrative support are some of the implementation challenges that have continued to dog the implementation, adoption and use of OERs in Kenyan schools. The findings of the present study will go a long way in providing useful insights to the developers of OERs and Kenyan education stakeholders in devising strategies of maximum utilisation of OERs in the Kenyan school system.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".