Exploring the socio-cultural aspects of e-learning delivery in Saudi Arabia
Bibliographic record
Abstract
Purpose This study aims to explore the socio-cultural aspects of e-learning delivery in Saudi universities from the perspectives of universities’ instructors and expert designers from the Ministry of Education. More specifically, this study examined the opportunities and challenges faced in the development of online learning environments at Saudi universities from a socio-cultural perspective. Design/methodology/approach This qualitative research study addressed pervasive socio-cultural challenges connected to e-learning delivery in Saudi Arabia. Data collection methods consisted of 28 in-depth insider expert interviews as well a thematic analysis of documents related to socio-cultural aspects of e-learning delivery in Saudi Arabia. Findings Findings from the data analysis uncovered two main thematic areas connected to e-learning delivery in Saudi Arabia, namely, culture and female access to e-learning. Research limitations/implications This research contributes original knowledge to international online learning research about the social and cultural complexity connected to online learning development in Saudi Arabia, as well as in other areas of the Arabic world where similar e-learning development initiatives are underway. Practical implications This research contributes original knowledge to international online learning research about the social and cultural complexity connected to online learning development in Saudi Arabia, as well as in other areas of the Arabic world where similar e-learning development initiatives are underway. Social implications This research contributes unique knowledge about the social and cultural complexity connected to online learning development in Saudi Arabia, as well as in other areas of the Arabic world where similar e-learning development initiatives are underway. Originality/value The interaction between Saudi culture and online learning has nurtured a unique learning model that adapts to cultural values to provide a quality learning experience.
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How this classification was reachedexpand
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".