Examining sources of variability in repeaters’ L2 writing scores: The case of the PTE Academic writing section
Bibliographic record
Abstract
This study aimed to examine the sources of variability in the second-language (L2) writing scores of test-takers who repeated an English language proficiency test, the Pearson Test of English (PTE) Academic, multiple times. Examining repeaters’ test scores can provide important information concerning factors contributing to changes in test scores across test occasions. Data consisted of the scores and background data (e.g., gender, age) and other covariates (e.g., context, interval between tests, number of tests attempted) for a sample of 1,000 test-takers who each took PTE Academic three times or more. Multilevel modeling was used to estimate the contribution of various factors to variability in repeaters’ PTE Academic writing scores across test-takers and test occasions. The findings indicated that changes in PTE Academic writing scores followed a quadratic trajectory (i.e., initial score increases followed by a decline) and that, as expected, test-taker initial overall English language proficiency (as measured on other sections of the test) was the strongest predictor of differences in PTE Academic writing scores at test occasion one as well as variance (across test-takers) in the rate of change in writing scores over time. Measures of retesting effects were not significantly associated with changes in writing scores, while test-taker factors (e.g., age, gender, and purpose for taking the test) were significantly associated with writing scores at test occasion one, but not with the rate of change in writing scores over time. The study highlights the value of examining repeater’ L2 test scores and concludes with a call for more research on the sensitivity of L2 proficiency tests to changes in L2 proficiency over time and in relation to L2 instruction.
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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 | 0.017 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".