New Ways of Mediating Learning: Investigating the implications of adopting open educational resources for tertiary education at an institution in the United Kingdom as compared to one in South Africa
Bibliographic record
Abstract
Access to education is not freely available to all. Open Educational Resources (OERs) have the potential to change the playing field in terms of an individual’s right to education. The Open University in the United Kingdom was founded almost forty years ago on the principle of ‘open’ access with no entry requirements necessary. The University develops innovative high quality multiple media distance-learning courses. In a new venture called OpenLearn, The Open University is making its course materials freely available worldwide on the Web as OERs ( see http://www.open.ac.uk/openlearn). How might other institutions make use of these distance-learning materials? The paper starts by discussing the different contexts wherein two institutions operate and the inequalities that exist between them. One institution is a university based in South Africa and the other is a college located in the United Kingdom. Both institutions, however, deliver distance-learning courses. The second part of the paper discusses preliminary findings when OERs are considered for tertiary education at these two institutions. The findings emphasise some of the opportunities and challenges that exist if these two institutions adopt OERs.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".