Examining Performance on an Integrated Writing Task from a Canadian English Language Proficiency Test
Bibliographic record
Abstract
Many English language proficiency (ELP) tests used for university admissions and placement now include integrated writing tasks that require examinees to use external sources when writing. Integrated writing tasks improve test authenticity and impact, but they raise several validity questions, such as what academic language skills they engage and whether performance on these tasks varies with examinee ELP level. This study addresses these questions with reference to an integrated writing task from the Canadian Academic English Language (CAEL) Test that involves reading, listening, and writing in academic contexts. Responses by 59 students to one of the CAEL integrated writing tasks are analyzed in terms of various grammatical, discourse, sociolinguistic, strategic, content, and source use aspects and compared across ELP levels (high and low) and score levels. The findings indicate that both ELP level and score level had significant effects on most writing features examined in the study, except for syntactic complexity. Additionally, except for syntactic complexity, all writing dimensions examined in the study were significantly associated with writing scores. The findings and their implications for the validity argument of source-based writing tasks are discussed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".