The Academic Achievement of Undergraduate Students with Different English Language Proficiency Profiles
Bibliographic record
Abstract
Many English-medium universities employ a compensatory model to establish cutscores on English language proficiency tests for student admissions. In this model, students can have different scores on different sections of the test provided their overall score meets the admission cutscore. This practice raises questions regarding potential variation in academic achievement among students with diverse score profiles. To address these questions, this study compared the demographic characteristics and academic achievement of 3,694 undergraduate students who met the required cutscore on the IELTS-Academic for admission to a Canadian English-medium university but have different scores on different sections of the test. The findings indicated that, generally, students with medium reading scores combined with low or medium writing scores exhibited lower academic achievement compared to those with high scores on all the IELTS sections or high reading scores combined with medium or high writing scores. The profile groups demonstrated significant differences in certain demographic characteristics, potentially explaining why they have different proficiency levels in different language skills. The implications of the findings are discussed, including whether to maintain the compensatory model or switch to a mixed one, and the implications for providing English language support to students with diverse proficiency profiles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 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".