Bibliographic record
Abstract
This paper is devoted to examining the factors responsible for the low achievement in English as a foreign language (EFL) among Saudi students. While some of these factors are demographic variables that pertain specifically to the learners themselves, such as gender, age, motivation, attitudes, aptitude, anxiety, autonomy, learning strategies, and learning style, most are external and outside the learners’ control. These external factors are particularly represented by sociocultural factors, such as the influence of Arabic as the first language (L1); religion, culture, and society; instructional variables, such as teacher behavior and teaching styles, the curriculum, and the teaching methods; and finally, problems with the educational system in Saudi Arabia, such as overcrowded classes, lack of teacher training, and a lack of technology. This paper begins by emphasizing the importance of English language learning for Saudis, followed by an analysis and a discussion of the factors that might explain their lack of achievement. The paper concludes by presenting some implications and offering recommendations for EFL practitioners and policymakers in the Kingdom of Saudi Arabia (KSA) to address the factors contributing to low EFL achievement among Saudi learners.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".