Barriers in Implementing E-Learning in Hormozgan University of Medical Sciences
Bibliographic record
Abstract
BACKGROUND: E-learning provides an alternative way for higher educational institutes to deliver knowledge to learners at a distance, rather than the traditional way. The aim of this study is to identify the barrier factors of e-learning programs in Hormozgan University of Medical Sciences (HUMS) in respect of the students and lecturers' point of view. METHODS: A cross-sectional study based on a questionnaire was conducted among 286 of students and lecturers in the nursing, midwifery and paramedic schools of HUMS. Two hundred and eighty-six participants filled in the questionnaire: 256 students, and 30 lecturers. RESULTS: Results of the study showed a lack of proper training in e-learning courses of the university 182 (69.1%), limited communication with the instructor 174 (68%) and the learners dominance of English language 174 (68%) showed the greatest importance for the students. The awareness about e-learning program was 80% and 43% among lecturers and students respectively.The dominance of English language 26 (86.7%) and lack of research grants for e-learning 23 (76.6%) and lack of proper training on e-learning courses from the university 20 (66.7 %) were the most important barrier factors of implementing e-learning for lecturers. E-learning courses to supplement classroom teaching was a solution that mentioned by the majority of students 240 (93.8%) and lecturers 29 (96.7%) in this study. CONCLUSIONS: The positive perception of e-learning is an important consequence effect in the future, educational development of nursing, midwifery and paramedic schools.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".