The Model of "Plan Do Check and Act" to Improve Chinese EFL Learners' Writing Strategies
Bibliographic record
Abstract
PDCA (Plan Do Check and Act) is a continuous cycle improving writing strategies on quality check. The cycle can be used in varied writing stages. The study explores the function of PDCA on writing class and the role on writing training. The circle of PDCA process was used in each of the teaching steps. In this study, the following four specific questions are to be answered: 1) What strategies are the most frequently employed by non-English majors in English writing? 2) What strategies does PDCA exert on? 3) Is there any difference between the experimental group and contrast group in writing strategies use? If there is, what is it? 4) Is there any difference in writing proficiency between experimental group and contrast group? The purpose of the research is to discover the effect of the use of PDCA on the enhancement the writing skills, which will have positive predictors on writing achievements. The findings testified after treatment there produced great significant difference between the experimental group and contrast group on strategy use which have improved writing quality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".