The Washback of the New Writing Tasks in China’s National Matriculation English Test
Bibliographic record
Abstract
The reform of National College Entrance Examination in Zhejiang Province, China has aroused widespread attention since it was released in 2014. It is notable that new English writing test types were adopted in the English subtest. The continuation task and summary writing become a challenge as well as a promoter for English writing teaching and learning. This study aims to explore the washback effect of the reformed English writing test on the teaching and learning of English writing in high school in Zhejiang, China. Through the method of questionnaire and interview with both teacher and student participants, it was found that the new types of writing test, especially the continuation task, are better at reflecting students’ actual English proficiency and improving students’ writing and reading ability, compared with the writing tests before the reform. However, the study also demonstrated that some negative effects might be caused due to practical issues. It is expected that this study will shed some light on the teaching and learning of English writing in high school and become a reference for any further educational reforms in Zhejiang and other provinces in China.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".