Native- and nonnative-speaking EFL teachers’ evaluation of Chinese students’ English writing
Bibliographic record
Abstract
This study examined differences between native and nonnative EFL (English as a Foreign Language) teachers’ ratings of the English writing of Chinese university students. I explored whether two groups of teachers -expatriates who typically speak English as their first language and ethnic Chinese with proficiency in English -gave similar scores to the same writing task and used the same criteria in their judgements. Forty-six teachers -23 Chinese and 23 English-background -rated 10 expository essays using a 10-point scale, then wrote and ranked three reasons for their ratings. I coded their reported reasons as positive or negative criteria under five major categories: general, content, organization, language and length. MANOVA showed no significant differences between the two groups in their scores for the 10 essays. Chi-square tests, however, showed that the English-background teachers attended more positively in their criteria to the content and language, whereas the Chinese teachers attended more negatively to the organization and length of the essays. The Chinese teachers were also more concerned with content and organization in their first criteria, whereas English-background teachers focused more on language in their third criteria. The results raise questions about the validity of holistic ratings as well as the underlying differences between native and nonnative EFL teachers in their instructional goals for second language (L2) 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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.000 |
| 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".