The Effects of Writing Strategy Instruction on EFL Learners’ Writing Development
Bibliographic record
Abstract
This study examined the effects of integrating writing strategy training into EFL writing instruction on learners’ strategy use and writing performance. Two classes of EFL adult learners participated in this study. The experimental group were instructed with the writing strategy training in their EFL writing class for 14 weeks, whereas the control group received the same EFL writing program without any explicit strategy training. Mixed methods were applied in collecting data. The quantitative instruments included the writing strategy questionnaires and writing performance tests, which were both pre-tested and post-tested in the both groups. The qualitative instruments of reflective journals were also conducted in the experimental group to probe deeper insights into learners’ strategy changes. The findings showed that there were significantly positive differences in learners’ using writing strategies and in writing proficiency favoring the experimental group. The findings of this study indicate that writing strategy training can be integrated in the EFL writing instruction, and can bring to positive impacts for learners’ strategic awareness and writing strategy use as well as their writing performance. The paper suggested the strong need to the process-based writing instruction, and writing strategy training holds promise in this regard.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".