Open and distance learning: What are the benefits for Africa, and what is its future?
Bibliographic record
Abstract
Abstract: This paper presents the results of a three-year longitudinal study (2007–2010) on open and distance learning (ODL) programs in Africa offered by the Agence universitaire de la Francophonie (AUF), the equivalent of the Commonwealth for French-speaking countries. We employed a mixed-method approach, including (1) an online questionnaire targeting students who were taking or had recently completed an ODL program offered by AUF in 2008 (N = 626); and (2) semi-directed individual phone interviews (N = 24). We performed descriptive and inferential analyses (SPSS) of the questionnaire data and a thematic analysis (QDA Miner) of the interview transcripts. In this presentation, we focus on the results concerning students ’ challenges and satisfactions as well as the benefits of ODL programs. We draw some conclusions about the pros and cons of ODL in the current African sociocultural context.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".