Bibliographic record
Abstract
As we know college English writing for the non-English majors is difficult to teach and it’s also hard to improve the study level of the students. The paper summarizes the achievements in teaching college English writing by the college English teachers in China and expounds the various teaching methods used in the classroom, including their advantages and disadvantages. The final aim of the paper is to discuss and work out a suitable teaching method for the Chinese students to improve their writing ability. Keywords: college English writing, the teaching of writing, teaching method of writing Resume Actuellement, il est difficile d’enseigner la composition en anglais dans les universites et les etudiants ont du mal a ameliorer leur niveau de composition. Vu cette situation, en synthetisant les fruits de recherche dans l’enseignement de composition des professeurs universitaires depuis ces dix dernieres annees, cet essai expose en detail les differentes methodes d’enseignement de la composition en anglais dans les universites chinoises et analyse leurs avantages et inconvenients dans le but de trouver un modele d’enseignement de la composition en anglais qui pourrait bel et bien elever le niveau de composition de nos etudiants et qui s’adapte a la situation nationale de notre pays. Mots-cles : composition en anglais universitaire, enseignement de la composition, methodologie de la composition 摘 要 針對中國大學生英語寫作難教、大學生寫作水準難提高的現狀，本文綜合整理中國近十年來高校教師的寫作教學研究成果，詳細闡述了在中國大學英語課堂所採用的各種不同的寫作教學法，分析了他們的長處和不足之處，旨在探討真正能夠提高我國大學生的寫作水準、適合中國國情的大學英語寫作教學模式。 關鍵詞：大學英語寫作，寫作教學，寫作教學法
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".