The Most Preferred and Effective Reviewer of L2 Writing among Automated Grading System, Peer Reviewer and Teacher
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
Who is the most preferred and deemed the most helpful reviewer in improving student writing? This study exerciseda blended teaching method which consists of three currently prevailing reviewers: the automated grading system(AGS, a web-based method), the peer review (a process-oriented approach), and the teacher grading technique (theproduct-oriented approach) in a Writing (IV) class involving 22 technological sophomore students of ModernLanguages Department. The questionnaire results indicated the participants preferred the teacher as the reviewer totheir peers followed by the automated grading system and considered the teacher the most effective in helping theirwriting. Three L2 teachers including one native speaker of English reviewed an essay which was the only and themost inconsistent case between a human rater and a machine rater in the study (2.3 vs. 3.6). This case surfaced anessential problem that the automated grading system couldn’t detect and correct expressions transferred from L1.Data also revealed that teachers without training, their grammatical error identification rates are respectively 82.9%,31.4% and 74.3%. After training, student reviewers could detect and correct from 70.2 to 79.3 percent of grammarerrors on average.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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