How Do Asian Candidates Fare in IELTS? A Look at 15 Years of Performance Data
Bibliographic record
Abstract
IELTS (International English Language Testing System) and its Asian test-takers have shared a heightening interdependency over the previous two decades. In order to achieve ambitions for an international education at Australian, Canadian, New Zealand, and UK universities, growing numbers of Asian prospective international students, particularly from China and India, find themselves required to undertake IELTS to demonstrate evidence of the sufficiency of their English language proficiency. Such is their importance to the tertiary education sectors of these countries that Asian candidates, who regularly constitute over 25 of the most common 40 cohorts of test-takers by nationality, have been key drivers of the more than ten-fold increase in the global IELTS candidature since 2003. The present study investigates how cohorts from 24 Asian nations fared in the Academic IELTS test from 2003 to 2018, utilising official performance data released by the IELTS partners. The study revealed that; 1) candidates from Hong Kong, Malaysia, and the Philippines registered the highest overall and section band scores across Asia; 2) the most sizeable overall score gains were made by Bangladeshi, Indonesian, and Jordanian candidates; and 3) worrying deteriorations in outcomes were exhibited by Emirati, Indian, and Iraqi test-takers. Implications of the findings are discussed in terms of Anglophone universities’ academic admission practices and the need for further research. Keywords: IELTS; English language proficiency; language testing; test-taker performance; international students
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".