Investigating Chinese EFL College Students’ Writing Through the Web-Automatic Writing Evaluation Program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
WWE-pigai is a kind of upgraded automated writing evaluation (AWE) system and there are 444,877,400 essays submitted and corrected on this platform. Some previous research on AWE system indicates that students do not tend to utilize AWE feedback to revise essays and improve writing abilities. The major objective of this study is to investigate Chinese EFL college students’ writing through the comparison of WWE-pigai and traditional writing method. The study lasts two terms and 120 Chinese colleges students participate in the research. The findings reveal that WWE-pigai can motivate EFL students to revise and resubmit their essays more than ten times, improve the scores, increase students’ grammar accuracy and vocabulary richness. The surface-level spelling errors (including punctuation mark misuse) are the most common for freshmen. WWE-pigai is not very effective to correct certain grammatical errors besides spelling and conjugation errors. For certain grammatical errors that the students cannot correct by themselves, the assistance of EFL teachers is necessary. We argue that the results reached through this study can offer useful implications for the usage of EFL writing strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it