Linking Cut-Scores Given Changes in the Decision-Making Process, Administration Time, and Proportions of Item Types Between Successive Administrations of a Test for a Large-Scale Assessment Program
Bibliographic record
Abstract
There is a continuing tension in testing programs to equate forms and maintain score scales and at the same time allow for changing conditions in the educational system, such as curriculum shifts or practical limits on testing time. When such changes occur, psychometric staff members are challenged to develop linking methods that allow for comparable reporting but meet requirements for psychometric rigor. This article describes a method addressing such shifts in testing programs. The application of the method is demonstrated on a large-scale educational testing program that had changes in test length, content distribution, and decision-making process. The method used to accomplish the linkage was to develop a pseudo test from the items included in the longer test before the change that was designed to mimic the test after the change. The linking of the tests using the pseudo test process resulted in a percentage of successful students that was similar to the percentages obtained prior to the changes. The linked scores were treated as comparable rather than equated scores.
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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 | 0.068 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".