Do ESL Essay Raters' Evaluation Criteria Change With Experience? A Mixed‐Methods, Cross‐Sectional Study
Bibliographic record
Abstract
This study adopted a mixed‐methods cross‐sectional approach to identify similarities and differences in the English as a second language (ESL) essay holistic scores and evaluation criteria of raters with different levels of experience. Each of 31 experienced and 29 novice raters rated a sample of ESL essays holistically and analytically and provided written explanations for each holistic score they assigned. Score and qualitative data analyses were conducted to identify the criteria that the raters employed to rate the essays holistically. The findings indicated that both groups gave more importance to the communicative quality of the essays than to other aspects of writing. However, the novice raters tended to be more lenient and to give more importance to argumentation than the experienced raters did. The experienced raters tended to be more severe, to give more importance to linguistic accuracy, and to refer to evaluation criteria other than those listed in the rating scale more frequently than the novices did. The article concludes with a call for longitudinal research to investigate to what extent, how, and why rater evaluation criteria change over time and across contexts.
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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 | 0.054 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".