The Most Preferred and Effective Reviewer of L2 Writing among Automated Grading System, Peer Reviewer and Teacher
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.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".