The Role of E-learning in Studying English as a Foreign Language in Saudi Arabia: Students’ and Teachers’ Perspectives
Bibliographic record
Abstract
Over the past few decades, there have been tremendous increase in technology advancement and the significance of this in the field of education cannot be overemphasised. The adoption and use of E-learning in studying EFL, in particular, is one such areas that has experienced such fast-paced development for some time now. As a result, the government all over the world are committing a lot of resources to keep up with this technology advancement. In this light, the government of Saudi Arabia through its Ministry of Education has recently made commitment, both as the practical and policy levels, with the hope to also benefit from using E-learning in studying EFL in Saudi Schools. However, little is known about the perception of students and teachers regarding the role of E-learning is studying EFL in the Saudi context. In an attempt to contribute to this research base, this paper draws on an empirical investigation using group interviews with students and teachers in order to gain insight into their perception about the role of E-learning in studying EFL in Saudi Arabia. The findings are presented and discussed in four thematic areas: promoting key learning skills, independent learning, flexible learning and interactive learning. The paper also highlights the limitations of the research and concludes by making a number of recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".