Longitudinal Trends in Academic Success: The Impact of IELTS and EAP Program Outcomes on International Student Performance
Bibliographic record
Abstract
Increased international student mobility has resulted in unprecedented numbers of English as additional language speakers applying to English medium of instruction universities. Consequently, it has never been more crucial to understand the relationship of language test scores, English for academic purpose (EAP) program outcomes, and academic achievement in order to make fair admission decisions for students. The current study takes a longitudinal approach to examine this relationship. Previous studies have typically taken a cross-sectional approach, focussing on the first one or two years of study. There is a dearth of research on the longitudinal relationship of language test scores, EAP program outcomes, and academic performance, and the present study examined this relationship over the course of a 4-year undergraduate degree. The study used multilevel modelling to examine the relationship of student academic performance data as measured by annual grade point average (GPA) and language test scores and EAP program outcomes. The study also used regression analysis to examine the relationship of language test scores, EAP program performance, and graduation rates. Findings highlight the importance reading subtest scores play in explaining the variance in first-year GPA and in GPA over time. Results suggest that high reading and writing scores are associated with higher GPA in Year 1. For students with lower reading scores and lower EAP program performance, their GPA improves over time suggesting that these students catch up. These results indicate different academic trajectories for students depending on their reading score and EAP program outcome and have implications for cut score decisions. Additionally, EAP program outcomes explain a larger amount of variance in Year 1 GPA than language test scores, which suggests EAP programs have significant impact on student academic performance. Finally, results also suggest that higher EAP program outcomes are significantly associated with a higher probability of graduating as well as graduating in a shorter amount of time, while test scores do not share this relationship. These results seem to indicate that EAP program outcomes may measure different constructs than language test scores and that more research is needed to understand the constructs that EAP program outcomes are measuring.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".