DIF investigations across groups of gender and academic background in a large-scale high-stakes language test
Bibliographic record
Abstract
High-stakes pre-entry language testing is the predominate tool used to measure test takers’ proficiency for admission purposes in higher education in China. Given the important role of these tests, there are heated discussions about how to ensure test fairness for different groups of test takers. This study examined the fairness of the Graduate School Entrance English Examination (GSEEE) that is used to decide whether over one million test takers can enter master’s programs in China. Using SIBTEST and content analysis, the study investigated differential item functioning (DIF) and the presence of potential bias on the GSEEE with aspects to groups of gender and academic background. Results found that a large percentage of the GSEEE items did not provide reliable results to distinguish good and poor performers. A number of DIF and DBF functioned differentially and three test reviewers identified a myriad of factors such as motivation and learning styles that potentially contributed to group performance differences. However, consistent evidence was not found to suggest these flagged items/texts exhibited bias. While systematic bias may not have been detected, the results revealed poor test reliability and the study highlighted an urgent need to improve test quality and clarify the purpose of the test. DIF issues may be revisited once test quality has been improved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".