Exploring the Impact of Online Translation on Writing Revision among Chinese Non-English Major Students
Bibliographic record
Abstract
The present study examined the efficacy of online translation as an auxiliary revision tool for Chinese non-English majors, assessing its influence on writing performance and the students’ perceptions of its role in the revision process. This study employed writing task, questionnaire survey, and semi-structured interview to examine the efficacy of online translation as an auxiliary revision tool for Chinese non-English majors, assessing its influence on writing performance and the students’ perceptions of its role in the revision process. A total of 94 Chinese non-English majors who learn English as a foreign language (EFL) participated in the study. The results indicate that compared with those who revise their texts independently, those Chinese non-English majors who refer to online translation in the process of revision, made significantly more changes at the below-clause and clause and above levels in terms of revision domain, with increased additions and substitutions. Additionally, having access to online translation in the process of revision, Chinese non-English majors significantly increased their text length in final drafts, with decreased error rate, more low-frequency and sophisticated words, and varied lexical choices. Besides, virtually most of the participants use online translation frequently in English writing activities with a generally positive attitude towards the integration of online translation into English writing revision. For English teachers, they can consider allowing students to use online translation in the process of revision and giving students sufficient guidance on how best to realize its fullest potential. For Chinese non-English majors, they’d better keep improving their self-learning ability and double-check the online translation output by using other resources.
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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 | 0.004 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".