An Investigation of the Accuracy of Alternative Methods of True Score Estimation in High-Stakes Mixed-Format Examinations
Bibliographic record
Abstract
Increasingly, high-stakes large-scale examinations are used to make important decisions about student achievement. Consequently, it is equally important that scores obtained from these examinations are accurate. This study compares the estimation accuracy of procedures based on classical test score theory (CTST) and item response theory (Generalized Partial Credit model, GPCM) for examinations consisting of multiple-choice and extended-response items. Using the British Columbia Scholarship Examination program, the accuracy of the two procedures was compared when the scholarship portions of the examinations were removed. For the subset of examinations investigated, the results indicate that removing these scholarship portions led to an error rate of approximately 10% with approximately seven out of 10 errors resulting in the denial of scholarships. The results were similar for both the CTST and the GPCM, indicating that for mixed-format examinations the two procedures produce randomly equivalent results. Implications for policy and future research are discussed.
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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 | 0.156 | 0.638 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".