Chinese College EFL Learners’ Cognition and Behavior in Relation to the Use and Acquisition of English Punctuation Marks
Bibliographic record
Abstract
Correct use of punctuation marks could help deliver expressive messages and improve logical clarity and discourse coherence. Hence it is also one of the important indicators to measure writing performance. Theoretical and empirical research on ESL/EFL writing has been fruitful, but fewer have focused on the use and acquisition of punctuation by English learners. The present research investigates Chinese EFL learners’ use and acquisition of English punctuation marks. To investigate college students’ self-reported perception, attitude, and behavior in relation to English punctuation marks, the researchers mainly used questionnaires and interviews as research tools, combined with classroom observation and students’ writing samples. It’s found that most Chinese English learners have recognized the importance of English punctuation and have expressed a strong willingness to learn, which is in stark contrast to their lack of learning and the poor self-evaluation of use. Based on the research results, we put forward constructive advice on the learning/acquisition of English punctuation marks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".