Online Learning in a South African Higher Education Institution: Determining the Right Connections for the Student
Bibliographic record
Abstract
Online learning is a means of reaching marginalised and disadvantaged students within South Africa. Nevertheless, these students encounter obstacles in online learning. This research investigates South African students’ opinions regarding online learning, culminating in a model of important connections (facets that connect students to their learning and the institution). Most participants had no prior experience with online learning. Their perceptions and barriers to learning may apply to other developing countries as well. A cross-sequential research design was employed using a survey among 58 fourth-year students who were studying a traditional paper-based module via open distance learning. The findings indicated certain essential connections: first, a strong social presence (through timely feedback, interaction with facilitators, peer-to-peer contact, discussion forums, and collaborative activities); second, technological aspects (technology access, online learning self-efficacy, and computer self-efficacy); and third, tools (web sites, video clips). The study revealed low levels of computer/internet access at home, which is of concern in an ODL milieu heading online. Institutions moving to online learning in developing countries should pay close attention to their students’ situations and perceptions, and develop a path that would accommodate both the disadvantaged and techno-savvy students without compromising quality of education and learning. The article culminates in practical recommendations that encompass the main findings to help guide institutions in developing countries as they move towards online teaching and learning.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".