Investigating Chinese EFL College Students’ Writing Through the Web-Automatic Writing Evaluation Program
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| 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 | 0.000 |
| 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 | 0.001 |
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".