Analytical Insights on the Position, Challenges, and Potential for Promoting OER in ODeL Institutions in Africa
Bibliographic record
Abstract
This paper shares analytical insights on the position, challenges and potential for promoting Open Educational Resources (OER) in African Open Distance and eLearning (ODeL) institutions. The researchers sought to use a participatory research approach as described by Krishnaswamy (2004), in convening a sequence of two workshops at the Open University of Tanzania (OUT) as a strategy for collecting data to obtain the aforementioned insight. The principal workshop objectives were to analyse the existing status of OER at the OUT and subsequently to share lessons learned in OER creation and production, integration and use, and hosting and dissemination. Other objectives were to discuss the rationale for an institutional OER policy and identify a suitable work-flow process for developing OER at the OUT. The workshop participants were purposively selected for their experience in co-developing OER materials with various outside organisations. The study included 28 representatives of the OUT academic units, and one facilitator from OER Africa. Research techniques used to collect data included a questionnaire, focused group discussions, presentations, and panel discussions. Results indicated that OUT staff were willing to engage with OER but had limited awareness, skills and competencies in the creation, integration and use of OER. The outcome of the study was the development of nine draft OER resolutions expressing needs that include the development of a comprehensive institutional OER policy related to existing institutional policies in order to guide, support and promote research and sustainable OER practice via holistic participation. Enabling strategies included capacity building, increased internal and external collaboration, and enhanced access to and visibility of OER via the institutional repository.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".