An Action Research on Improving Non-English Majors’ English Writing by Basic Sentence Pattern Translation Drills
Bibliographic record
Abstract
English writing plays an indispensible part in EFL (English as a Foreign Language) learning for Chinese students, which accounts for a high score in an English test in China. And it is also a comprehensive reflection of students’ abilities in L2 application. However, most non-English majors in vocational and technical colleges have great trouble in English writing and writing incorrect and inappropriate sentences ranks number one among all the English writing problems. English writing teaching is always a weak part in English teaching. The researcher conducted an eleven-week action research on basic sentence pattern translation drills among 50 non-English majors from 4 classes who didn’t pass CET-3 in a Vocational and Technical College. Before the action research, students’ writing problems were identified via questionnaire, sentence test and writing pretest. Then an eleven-week action plan was carried out and one adjustment was made to the plan in the light of results of interviews. Writing posttest was taken and another interview was made afterwards. It was found from data collection and analysis as well as analysis of students’ writing samples that students could write correct sentences in English and their English writing scores and abilities improved a lot after the action 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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".