The Sequence of Modules: A Facet in Language Proficiency Testing
Bibliographic record
Abstract
Given the widespread application of language proficiency examinations and their gate-keeping function, it is of utmost importance for researchers and test developers alike to identify and eliminate any facet of these tests which could be a potential source of invalidity or unreliability. One such facet is the sequence through which the four language skills are presented to the applicants. The present body of literature indicates that there has been no investigation into the order of skills in language proficiency tests. What is more, two established language proficiency tests, namely the International English Language Testing Service (IELTS) and the Test of English as a Foreign Language (TOEFL) present their skills in different sequences. This study sets out to determine whether altering the sequence of two skills on a language proficiency test (in this case, the IELTS) would result in any difference in applicants' performance on each individual skills. To this end, 120 learners of English as a Foreign Language (EFL) were asked to take part in two consecutive administrations of the IELTS, each time with a different sequence. The findings revealed that although intermediate and advanced learners performed equally well on both administrations, there was a significant difference in the performance of elementary learners across tests.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".