Research on the Application of “Tree Analysis Diagram” to the Teaching of English Argumentative Writing of the Chinese EFL Learners
Bibliographic record
Abstract
Writing as one of essential skills in English learning is attached more and more importance. English writing involves not only the application of lexicon and grammar, but also the construction of the text and the expression of the thought. For Chinese EFL learners, the common problem in English writing is that they tend to apply the Chinese thinking pattern and organizational pattern to wording, phrasing and even the text construction. In other words, Chinese EFL learners lack English thought pattern. Based on that, the researcher puts forward the “tree analysis diagram” to help Chinese EFL learners acquire the English thinking pattern. The current research compares the differences between the Chinese thinking pattern and the English thinking pattern; analyzes the effect of these differences on English writing and verifies the effectiveness of the “tree analysis diagram” in helping Chinese EFL learners developing the English thinking pattern and improving the quality of English writing by an experiment. The results of the research showed that the Chinese thinking pattern influences students’ English writing and the main problem is that the organizational pattern and the logic of the writing are not clear. After the application of the “tree analysis diagram”, the results showed that “tree analysis diagram” to some extent can help Chinese EFL learners avoid the influence of the Chinese thinking pattern; improve the ability of composing English writings with the English thinking pattern; develop the habit of conceiving and writing in English; arouse the interest for English writing and eventually improve the quality of English writing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| 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 | 0.002 |
| 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".