Investigating Difficulties Grade 10th Students Face in EFL Writing Classes: The Case of Aman Secondary and Preparatory School
Bibliographic record
Abstract
Objective: This study investigated the difficulties Grade 10 students face in EFL writing classes at Aman Secondary and Preparatory School. Methods: A descriptive‑survey design was used. Eight Grade 10 English teachers were selected through a comprehensive sampling, and 97 students were chosen by simple random sampling. Five instruments gathered data: (1) a free‑writing test (document analysis), (2) a questionnaire with open‑ and closed-ended items, (3) classroom observation, (4) teacher interviews, and (5) student focus‑group discussions. Data were analysed qualitatively and quantitatively. Results: Key writing problems were poor organisation, limited vocabulary, faulty grammar, weak idea generation, and errors in punctuation, capitalization, and spelling. Contributing factors fell into four categories: Student-related, poor writing background, lack of practice, low interest, and overemphasis on language form. Teacher-related, limited attention to writing, low proficiency in teaching it, minimal guidance and feedback, and exclusion of practical writing from assessments. Class‑size overcrowded rooms that hinder individual support. Instructional‑material, an overly bulky textbook with too few relevant exercises and unfamiliar contexts. Conclusion: Writing instruction currently receives scant attention: students’ competence is low; teachers’ methodological proficiency needs improvement; large classes restrict practice; and textbook activities require revision. Stakeholders must prioritise writing by providing targeted teacher training, reducing class size, refining materials, and ensuring regular, practice-oriented assessment. Without such action, students will continue to miss a critical language skill.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".