The Effects of Institutional Support Factors on Lecturer adoption of eLearning at a Conventional University
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Conventional Higher Education Institutions in Kenya are in the process of implementing eLearning projects. These initiatives are, however, fraught with challenges. At the Maseno University eCampus, an evaluation of statistics on the institutional LMS after two years of implementation revealed that many lecturers had minimal or no log-in statistics, an indication that there was a gap in the adoption of eLearning. This study investigated factors explaining lecturer adoption of eLearning. A sample of 55 lecturers was selected and a questionnaire administered on their personal and institutional support factors for eLearning adoption. The findings revealed that the lecturers had a positive perception of the usefulness of eLearning and high self-efficacy in the adoption of eLearning. The gap in eLearning adoption was perceived by respondents to be a result of inadequate institutional support. The results suggest that lecturers are likely to be better adopters of eLearning not only when knowledge management processes and policies related to eLearning are developed but also where the institution works towards building and supporting a community of eLearning adopters.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it