Test Takers’ Attitudes Toward Varieties of Accents in Listening Tasks of the Duolingo English Test (2021 test version)
Bibliographic record
Abstract
There has been much debate in assessment research about the inclusion of Global English accents in high-stakes listening tests. This study explored test-takers’ attitudes toward the inclusion of different English accents in the Duolingo English Test (DET) 2021 test version and their associations with listening test scores. One hundred sixty English learners from four language backgrounds (Chinese, Korean, Hindi, and Latin American Spanish) completed yes/no vocabulary and dictation tasks that simulated the listening sections of the DET. The tasks included speech produced by English speakers of the same language background as the listeners, as well as American and British English. Learners completed a survey that elicited their attitudes toward non-standard English accents in proficiency tests. Exploratory factor analysis of survey responses revealed two contrasting trends in learners’ attitudes. Constructed responses suggested that while listeners generally preferred prestigious English models (e.g. American English or British English), they also expressed a need for incorporating other accent varieties. The relationships between listeners’ attitudes and their performance on the test were minimal (r < .26). The findings hint at a deeper understanding of test takers’ needs regarding accent varieties in listening tests. The study offers implications for the development of high-stakes English listening tests in global contexts.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".