The Lack of Qualified EFL Teachers in Saudi Schools: A Qualitative Interview Study
Bibliographic record
Abstract
Despite teaching English language in Saudi Arabia for 6 decades, yet the outcomes are unsatisfactory. In this article, the lack of qualified English teachers in Saudi Arabia is the main reason for causing that issue. To address the issue, this study attempts to understand how untrained teachers have a negative influence on students' performance and competence. Unstructured interviews were conducted with students from two Saudi schools to reflect on the problems and obstacles that Saudi pupils encounter as a result of their incompetent instructors. Instead of interviewing instructors, we interviewed students to know the strategies, methods, and techniques employed by their teachers, which resulted in their incapacity to improve. According to the participants, the majority of English teachers in Saudi schools speak Arabic in English lessons rather than English. They also employ traditional approaches such as the teacher-centered method. They educate and explain in English, but when they reach a major obstacle, they immediately switch to Arabic as an efficient option. The study's findings highlight the necessity of having trained teachers to teach English in EFL programs in Saudi Arabia. The study's objective is to demonstrate that instructors are at the heart of the issues that affecting students' growth and how to assist them to overcome those problems.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| 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".