Applying UNESCO Guidelines on Mobile Learning in the South African Context: Creating an Enabling Environment through Policy
Bibliographic record
Abstract
Mobile broadband penetration is growing rapidly in Africa, and it offers vast opportunities for mobile learning. Together with its possibilities is the danger of overlooking standards related to its use. The United Nations Educational, Scientific and Cultural Organisation (UNESCO) has initiated work in this area focusing on African and Middle East (AME) countries. Countries are required to develop their own mobile learning policies. Examining information and communication technology (ICT) in the South African education environment, a qualitative approach is adopted using a literature review to assess the relevance and applicability of mobile learning in the broader education environment. A thematic analysis is used to identify themes from UNESCO’s guidelines, which are compared to the South African environment. Tracing the use of technology by an open distance learning (ODL) provider, an adapted framework was developed for mobile learning. This article argues the need to create an environment that enables sustainable mobile learning provision through policy development.
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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 | 0.045 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".