English in Saudi Arabia: Status and Challenges in The Light of Prince Mohammad Bin Salman’s Vision 2030
Bibliographic record
Abstract
It is essential that educational institutions prepare students for the workforce especially when they are teaching English. In most Saudi Universities English Departments have been established in the Faculties of Arts, Languages, Education and Translation. However, recognition of the need for English in the Saudi educational system has not always been matched by acceptable educational outcomes. This is indicated by the inadequate number of well-trained and highly qualified teachers of English. Lack of recognition has hindered progress towards reaching the Kingdom Vision of 2030 that focuses on empowering citizens through reshaping the educational system and turning learners into skillful, educated and independent individuals. Therefore, this study examines the extent to which the KSA Vision 2030, in terms of teaching English as a foreign language in universities, is being implemented. A questionnaire was given to first year students at the Northern Border University, in Saudi Arabia. The questionnaire had two main sections, the first contained six general questions and the second section had 39 items covering very specific elements such as, Content & Teaching Methods, Evaluation & Assignments and Training & Professional Development. Analysing the data from the questionnaire was done using SPSS software.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".