Distance Learning Students’ Evaluation of E-Learning System in University of Tabuk, Saudi Arabia
Bibliographic record
Abstract
This study evaluates the experiences and perceptions of students regarding e-learning systems and their preparedness for e-learning. It also investigates the overall perceptions of students regarding e-learning and the factors influencing students’ attitudes towards e-learning. The study uses convenience sampling in which students of the Education & Arts and Business Administration colleges were e-mailed the survey. Of the distributed questionnaires, 500 completed were received and analysed. The findings revealed that the majority of the sampled participants used and benefited from the e-learning system. The results also indicated that students underwent an adequate training program provided by the University on the use of e-learning. Furthermore, the results disclosed that participants reasonably received technical support when they used electronic cards on e-learning web portals. In addition, regression analysis found that only recorded lectures help to compensate for the virtual class, manuals, instructions and guidelines published at web portals, and the easiness of e-learning system provided by the University were statistically significant with the positive attitude towards the e-learning system. The findings provided a preliminary framework for future studies on e-learning systems across Saudi universities. The findings also suggest administrators, researchers, decision makers, and policy makers should properly plan, design, implement, and promote e-learning with a clear vision in Saudi Arabia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".