Difficulties of Learning EFL in KSA: Writing Skills in Context
Bibliographic record
Abstract
This study is an in-depth effort to investigate issues usually faced by EFL learners in writing skills. Fifty students of Preparatory Year Program (from different sections) Najran University were randomly selected to illicit their opinion on writing skill using Likert’s 5 scale (always, usually, sometimes, rarely never) questionnaire. In addition, fifty writing samples from first and second midterms of PYP were also selected. The study analyses the writing samples mainly focusing on with special reference to capitalization, punctuation, language use (grammar) and spelling and their impact on other items of language like articles, preposition, coherence, cohesion etc. Contrastive analysis of students’ writing samples and questionnaire prove that students commit mistakes, though unconsciously, in writing. While conducting data analysis using students’ writing samples, it was observed that students used ways to pass an exam i.e. memorize the writing answer(s)/paragraph(s) rather than the proper approaches to developing/learning most common writing strategies when writing an answer. In the end, this study offers some remedial measures for writing problems of Arab EFL 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".