Understanding writing quality change: A longitudinal study of repeaters of a high-stakes standardized English proficiency test
Bibliographic record
Abstract
This study examined the writing score and writing feature changes of 562 repeat test takers who took the Canadian English Language Proficiency Index Program–General (CELPIP–General) test at least three times, with a short (30–40 day) interval between the first and second attempts and a longer (90–180 day) interval between the first and third attempts. Analysis was conducted to uncover whether changes occurred at different testing durations (short vs. long) and whether the observed changes varied across repeater’s initial proficiency groups (low, mid, high). The writing scores measured by CELPIP bands showed great stability over the 6-month period, but the trends of development differed by proficiency group. Low proficiency test takers were more likely to have faster observable score gains, compared to the medium proficiency group, whereas high proficiency repeaters may not maintain their score levels at later attempts. Writing quality was analyzed using natural language processing (NLP) tools. Results suggested that for all proficiency groups, lexical features were more likely to improve over the 6-month period, with some measures showing improvement at 1 month; features in cohesion and syntactic sophistication, however, did not change significantly.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".