Improving Writing Skills with Systemic Functional Linguistic Approach: The Case of Vietnamese EFL Students
Bibliographic record
Abstract
This study was conducted with the purpose to identify the effectiveness of Systemic Functional Linguistic approach to improve writing skills for the EFL students at a university in Hanoi, Viet Nam. The preliminary investigation showed that most students at this university experienced many difficulties in English writing skills and they were not motivated in writing lessons. To make situation better, an action research plan was conducted with the use of quantitative and qualitative methods, focusing on applying Systemic Functional Linguistic approach, typically Theme-Rheme patterns to raise the students’ awareness of Theme-Rheme benefits in creating logical text organization and then improve their writing skills. The subjects of the study were 30 students of English major at a university of foreign languages in Vietnam. The data were collected through the pre- and post-tests, questionnaire and semi-structured interviews. The findings of the study suggested that the use of this approach could improve the students’ writing skills and most of research students liked this technique because it made them motivated during English writing lessons.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".