Teaching English Paragraph Writing in EFL High School Contexts: Teachers’ Perceptions, their Classroom Practices and Students’ Voices
Bibliographic record
Abstract
This study explored Vietnamese EFL teachers’ paragraph writing instruction and the challenges students encountered in learning to write English paragraphs. The data were collected at five high schools in central Vietnam via questionnaires, interviews and classroom observations. In particular, 40 EFL high school teachers completed a questionnaire and five classroom observations were additionally made to five out of these questionnaire respondents to understand their actual classroom practices. The teachers were further interviewed at the post-teaching stage. In addition, 150 students from the participating high schools were surveyed for the difficulties they had with paragraph writing and five of them were interviewed for follow-up insights. The results uncovered that teachers reported a central focus on both global and local dimensions of a paragraph. In their teaching practices, they tended to employ a product-based rather than process-based approach by guiding students through the final end product of the written paragraph, followed by whole-class feedback. Time constraint and students’ low motivation to write and revise were reported as main barriers to the classroom adoption of a process approach. In students’ perceptions, starting a paragraph, generating and organizing ideas, and using appropriate sentence structures and lexical items were among the common challenges. The study suggests important implications for paragraph writing instruction and for future research.
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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 | 0.006 |
| 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 | 0.002 |
| 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".