Prospect of eLearning in Higher Education Sectors of Saudi Arabia: A Review
Bibliographic record
Abstract
The number of internet users in Saudi Arabia has increased rapidly from 7.7 million users in 2008, to 21.6 million in 2015. This is a result, in part, of the Saudi government's investment in information and communication technology infrastructure. In addition, the Saudi government spends between a quarter and a third of its budget on education every year. However, even with the number of Higher Education institutions increasing in Saudi Arabia, a significant number of students miss out on a place at a University. ELearning is one way to provide accessibility to more students and to overcome cultural barriers which may prevent some citizens from perusing a university qualification. This paper will focus on higher education in Saudi Arabia, in particular, the advantages and advances which are occurring in this country in terms of eLearning. Supportive departments created to help the educational processes to move toward eLearning, such as the National Centre of ELearning and Distance Learning and the Saudi Digital Library will be described and examined. Challenges in the field of education will then be examined and whether these challenges can be overcome by utilizing effective eLearning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".