Challenges of implementing e-learning in Kenya: A case of Kenyan public universities
Bibliographic record
Abstract
<p>In this paper, we discuss the challenges experienced by Kenyan public universities in implementation of e-learning and recommend possible solutions towards its successful implementation. In the last few years, most Kenyan public universities have adopted e-learning as a new approach to teaching and learning. However, the implementation challenges faced by these universities have continued to impact negatively on its effective utilization. This paper presents the findings from a survey of 148 staff of three Kenyan public universities who are currently using e-learning in blended mode approach. The purpose of this study was to investigate the challenges hindering the implementation of e-learning in Kenyan public universities. Data was collected through questionnaires, in-depth interviews and document analysis. The findings reveal that e-learning comes with some challenges that must be addressed by Kenyan public universities before successful implementation can be realized. However, the benefits and opportunities presented by e-learning far outweigh the challenges. The paper finally recommends some possible solutions that public universities could embrace towards successful implementation of e-learning.</p>
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".