An Analysis of Native Language Transfer in English Writing for Non-English Major Students
Bibliographic record
Abstract
Writing is considered to be an effective way to convey thoughts and feelings by written languages, which play a vital role in measuring learners’ comprehensive competence. As well, English writing is regarded as an indispensable item in English examinations, but actually college students’ writing performance is far from satisfaction. And it is suggested that native language transfer is one of the principal factors leading to the undesirable result. This assay adopts transfer theory, contrastive analysis and error analysis theory to serve the research. The purpose of the research is to explore the influence of native language transfer in English writing for non-English major students. It employed both qualitative and quantitative research including writing test, questionnaire and interview. The subjects in this research are 120 sophomores in Henan Polytechnic University majoring in Computer Science & Technology and Civil Engineering. The research is conducted from three aspects—lexis, syntax and discourse and there are great findings: compared with male students, female students depend less on native language in the writing process; due to native language transfer the number of errors students make in lexis ranks the first followed by errors in syntax and the then the errors at discourse level; the involvement of native language transfer varies with different stages of writing.
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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 | 0.019 |
| 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 | 0.000 |
| 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".