MOOCs and OER in the Global South: Problems and Potential
Bibliographic record
Abstract
This paper examines the problems and potential of Massive Open Online Courses (MOOCs) and Open Education Resources (OER) in the global South. Employing a systematic review of the research into the use of open online learning technologies in Southern contexts, we identify five interrelated themes emerging from the literature: 1) access to the Internet; 2) participant literacies; 3) online pedagogies; 4) the context of content; and 5) the flow of knowledge between North and South. The significance of Southern voice and participation is addressed in the final section, which concludes that on balance, the literature offers a qualified endorsement of the potential and actualities of MOOCs and OER in the global South. The ongoing tendency for the research literature to pay little heed to the agency of the social actors with the most to gain from these innovations is noted, opening up space for further research into the lived experience of online learners in the global South.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".