Pre-admission EAP course completion as evidence of English language proficiency for university admission: A longitudinal evaluation study
Bibliographic record
Abstract
To increase the number of international students, a growing number of English-medium universities offer multiple pathways for demonstrating English language proficiency (ELP) as part of the admission process. While the standard practice requires achieving a minimum score on a standardized ELP test, an alternative pathway entails completing one or more pre-admission English for Academic Purposes (EAP) courses successfully. This practice assumes that these students possess ELP levels comparable to those meeting the minimum score on an ELP test and that both pathway groups can anticipate achieving similar levels of academic achievement at university. This longitudinal study examines these assumptions by comparing the academic achievement (i.e., Grade Point Average [GPA]) over ten semesters of undergraduate students admitted to a large Canadian English-medium university after successfully completing pre-admission EAP courses with domestic students and those admitted via IELTS scores. The findings indicated that students admitted through the EAP program initially achieved lower GPA in the first semester. This gap endured throughout the ten semesters, albeit gradually narrowing over time. The implications of these findings 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".