A study of English Writing Test in Senior High School Entrance Examination on English Writing Teaching
Bibliographic record
Abstract
Writing plays a more and more important role in the English test of senior high school entrance examination, and teachers pay more and more attention to the teaching of writing. This paper attempts to start with the backwash effect of English writing test on English writing teaching, and further think about its enlightenment to English writing teaching, so as to give full play to the positive backwash effect of English writing test and minimize the negative backwash effect. Theoretical Significance: It provides a feasible teaching model and implementable design process for the current English writing teaching that pursues high scores. The integration of reading and writing based on the development of thinking quality is an important way to realize the cultivation goals mentioned in the English subject key competence, and it is a key direction to implement and deepen the concept of instrumental and humanistic courses. Through this teaching mode, students will be given a learning atmosphere of overall learning and positive thinking, and at the same time, the educational goal of developing quality education will continue to advance. Practical significance: It provides a new connection point for junior high school English teachers to teach in the reading-to-write method, and also provides a certain reference for the teaching design involved in each link part of teaching process. Through the research methods such as questionnaires, interviews and tests, we can compare the students’ classroom feedback and performance before and after, analyze and demonstrate their effectiveness in improving students’ writing ability and writing attitude. Then hope to provide a practical paths and teaching references for the future teaching.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".