Challenging Equivalency: A Two-Site Analysis of English Language Proficiency Pathways and Academic Achievement in Canadian Higher Education
Bibliographic record
Abstract
This dissertation investigates the relationship between English language proficiency (ELP) pathways and academic achievement among international undergraduate students at two Canadian universities (University A and B) and challenges the presumed equivalence between scores on standardized English ELP tests (IELTS and TOEFL) and the completion of English for Academic Purposes (EAP) programs. Given the scarcity of comparative research on each pathway’s effectiveness in predicting academic achievement, this study examined whether students admitted through different ELP pathways and at different proficiency levels (low to high) significantly differed in terms of their first-year cumulative grade point average (CGPA) and credits earned (CCE), while controlling for discipline, country of origin, gender, and age. By examining two institutions with similar ELP policies but distinct student populations, this study also explores how institutional context may moderate the relationship between ELP pathways and academic achievement. Using a comprehensive quantitative approach, with data from 9,673 students at University A and 2,480 at University B, analyses consisted of multiple regression, analysis of covariance and interaction analyses. ELP pathways were categorized based on test scores relative to admission cutoffs and EAP completion. Regression models progressively incorporated moderators to assess their impact on academic outcomes. Results indicated that students admitted through IELTS outperformed their peers from EAP programs in CGPA, though at University B, this trend was evident only among students with the highest IELTS scores. In contrast, the TOEFL pathway showed less consistent superiority compared to EAP students: only students with the highest TOEFL scores significantly exceeded EAP students in CGPA. For CCE, however, students admitted through IELTS and TOEFL surpassed EAP students across both institutions. Moderated by discipline, gender, age, and country of origin, outcomes varied by institution, highlighting context-specific effects. These findings indicate that students admitted through standardized tests typically outperformed those admitted through EAP, challenging presumed equivalency between pathways. Implications for policy and practice for Canadian universities are discussed to better ensure support for international students’ academic success.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".