Integration of Open Educational Resources in Higher and General Education Institutions: from the Perspectives of Specialized and Concerned Bodies in E-Learning
Bibliographic record
Abstract
Open educational resourses have become a strategic source of a high degree of importance and this explains the reason for the acceleration of countries to join the use of them, but unfortunately, the results of a survey study conducted in the Kingdom of Saudi Arabia on eight experts in e-learning showed a gap that hinders integration in the use of open educational resources among educational institutions, especially at the general and higher education. Accordingly, the present study aimed to review the most prominent Open Educational Resources (OER) platforms in Saudi Arabia and identify the reality of cooperation and the best means of integration between higher and general education institutions from the perspective of specialists and concerned bodies. It adopted the analytical survey (descriptive) method. It covered a population of specialists and concerned bodies in e-learning from higher and general education institutions. The study applied a questionnaire to a sample of (144) participants from higher education institutions and (327) participants from general education institutions. Finally, it concluded results, made recommendations and suggested further studies.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".