Uptake of OER by staff in distance education in South Africa
Bibliographic record
Abstract
Open Educational Resources (OER) emerged within the context of open education which is typically characterized by the sharing of knowledge and resources and the exchange of ideas. Unisa as a mega open distance learning (ODL) university has publicly communicated its intention to take part in the use and creation of OER. As global and local university research on OER is limited, this prompted an investigation to gauge the uptake of OER at Unisa, by staff, with the purpose of institutional information gathering for decision making and planning in this area. During 2014, a survey was undertaken for this reason. The survey examined knowledge of OER, Intellectual Property (IP) Rights and Licensing, participation in OER, barriers to OER and OER in the Unisa context with a view to determining the stage at which the institution is in terms of adopting and engaging with the OER initiative. The results indicated that although there is knowledge and understanding of OER, this has not been converted into active participation. It further highlighted the barriers that are prohibiting the operationalization of OER and resulted in recommendations for planning and activities in respect of OER. The constructs investigated and the results thereof might not be generalizable to other contexts, although commonalities are likely. The insights should prove useful to a variety of contexts. The paper illustrates the need for institutions, irrespective of context, to take stock of the impact of initiatives and in this case evaluate how the institution and staff mature through various phases in the uptake of OER in order to guide effective planning, decision making and implementation.
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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 | 0.017 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".