Investigating the E-Learning Challenges Faced by Students during Covid-19 in Namibia
Bibliographic record
Abstract
Over the past two decades, e-learning has become an increasingly important field of study that has attracted scholarly and policy makers’ attention. Many developing nations have embraced e-learning as a tool to enhance accessilibility and affordability of higher education. During the COVID-19 lockdown period, many universities across the world were forced to embrace online teachning and learning to circumvent lockdowns, social distancing and other public health interventions put in place to contain the spread of the novel coronavirus. Consequently, this study sought to establish students’ experiences with the e-learning mode during the COVID-19 lockdown in Namibia. The paper discusses the results of an online survey of 137 undergraduate students about their experiences using e-learning technologies during the COVID-19-induced university closures. An online survey instrument was created on Google forms and a link distributed to students through WhatsApp class groups. Quantitative data were presented through frequency tables and figures, whilst we adopted thematic content analysis to analyse qualitative data. The results of the survey indicate that mobile devices remained the primary computing device used to access academic information. An analysis of the study results led to the emergence of five themes, viz, e-learning system accessibility, e-learning platform layout, resources to access Internet and network, isolation and home environment that captured student challenges with online classes. This paper argues that e-learning is still faced by a myriad of challenges that need to be addressed if it has to be a success. Furthermore, we advance the argument for mobile learning as a viable option for Africa due to the ubuiquity of mobile devices.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".