The Impact of Socio-Cultural Issues for African Students in the South African Distance Education Context
Bibliographic record
Abstract
Open Distance Learning (ODL) takes place within different environments that are influenced by the social, cultural and political fields in which a student lives. This is particularly significant in South Africa where distance learning has been identified as the main system that should provide access to higher education for most students in the country. Through ODL, disadvantaged students can have access to higher education. This paper uses a socio-cultural framework to examine distance education students’ accounts of their experiences of learning. This study reveals aspects of the socio-cultural contexts that tend to be marginalised by ODL institutions. Resume L’apprentissage ouvert et a distance (AOD) se deroule dans divers environnements qui sont influences par les milieux social, culturel et politique dans lesquels un etudiant vit. Ceci est particulierement vrai en Afrique du Sud ou l’apprentissage a distance a ete identifie comme etant le principal dispositif pouvant offrir un acces aux etudes superieures, a la plupart des etudiants du pays. Par le biais de l’AOD, les etudiants moins fortunes peuvent acceder aux etudes superieures. Cette recherche emploie un cadre de reference socioculturel pour etudier les temoignages rendus par les etudiants au sujet de leurs experiences d’apprentissage. L’etude revele certains volets des contextes socioculturels qui tendent a etre marginalises par les etablissements d’AOD.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".