Challenges Experienced by Students Studying through Open and Distance Learning at a Higher Education Institution in Namibia: Implications for Strategic Planning
Bibliographic record
Abstract
Online learning remains one of the most powerful enablers and accelerators for realising higher education studies by enhancing teaching by means of innovative technologies and pedagogies. However, the success rate of students studying through Open and Distance Learning (ODL) remains very low. Therefore, institutions of higher learning in Namibia should continuously establish and assess the challenges affecting the students who opt to study via distance mode to devise strategies required to address such challenges. The objective of this study was to understand the challenges experienced by students studying through ODL at Higher Education Institution (HEI) in Namibia and establish the challenges they face. A qualitative, phenomenological, explorative, descriptive and contextual research strategy was employed in this study to explore and describe challenges experienced by students’ studying through ODL at HEI’ satellite campus in Namibia. A purposive sampling was utilised in the selection of participants. Data were collected from participants using semi-structured interviews with nine participants. Three themes were identified, namely, the reasons why students chose to study through ODL programme, challenges experienced by students studying through ODL and mechanisms for improvement. The findings of this study call for well-articulated plans and actions to address the challenges faced by students studying in the distance e-learning mode. The study recommended that both Lecturers and ODL programme Administrators should undergo refresher training on distance education annually to ensure that they are aware and can address the challenges faced by their students.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".