MétaCan
Menu
Back to cohort
Record W4412972797 · doi:10.1016/j.jeap.2025.101555

Pre-admission EAP course completion as evidence of English language proficiency for university admission: A longitudinal evaluation study

2025· article· en· W4412972797 on OpenAlexfundaboutno aff
Khaled Barkaoui, Jason Holmes, Saskia Van Viegen

Bibliographic record

VenueJournal of English for Academic Purposes · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
FundersYork University
KeywordsCourse (navigation)Medical educationLongitudinal studyEnglish for academic purposesEnglish languagePsychologyComputer scienceLinguisticsMathematics educationMedicineEngineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.380
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of English for Academic PurposesSame topicEFL/ESL Teaching and LearningFrench-language works237,207