An Action Research on the Effects of “Production-Oriented Approach” on Senior High School Students’ English Writing
Bibliographic record
Abstract
The present study explored the effects of “production-oriented approach” (POA) on senior high school students’ English writing. 8-week action research of English writing in a senior high school in Inner Mongolia, China was carried out. The data were collected through writing tests and the questionnaire to answer the following research questions: What are the effects of POA on the senior high school students’ English writing performance and their critical thinking skills (CTSs)? The research results show that: 1. teaching writing based on POA could improve the language and content of English writing. In specific, concerning language, POA could improve the syntactic complexity, lexical diversity, lexical variation of complexity, fluency, and accuracy of English writing. However, there was no significant effect on the structure of English writing. 2. POA positively influenced students’ CTSs, especially in the dimensions of analysis, curiosity, justice, cognitive maturity, open-mindedness, and tenacity. However, the writing teaching with POA did not influence the dimensions of truth-seeking and self-confidence. Finally, combined with the research findings, some suggestions for improving senior high school students’ English writing and CTSs were put forward, and the limitations of this research and directions for the future research were discussed.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".