Evaluating the Bookmark Standard Setting Method: The Impact of Random Item Ordering
Bibliographic record
Abstract
Throughout the world, cut scores are an important aspect of a high-stakes testing program because they are a key operational component of the interpretation of test scores. One method for setting standards that is prevalent in educational testing programs—the Bookmark method—is intended to be a less cognitively complex alternative to methods such as the modified Angoff (1971) Angoff, W. H. 1971. “Scales, norms, and equivalent scores”. In Educational measurement , 2nd ed., Edited by: Thorndike, R. L. 508–600. Washington, DC: American Council on Education. [Google Scholar] approach. In this study, we explored that assertion for a licensure examination program where two independent panels applied the Bookmark method to recommend a cut score on its Written Exam. One panel initially made their ratings using an ordered item booklet (OIB) in which items were randomly ordered with respect to empirically estimated difficulty followed by judgments on a correctly ordered OIB. A second panel applied the Bookmark process with only the correctly ordered OIB. Results revealed striking similarities among judgments, calling into question panelists’ ability to appropriately engage in the Bookmark method. In addition, under the random-ordering condition, approximately one-third of the panelists placed their bookmarks in a manner inconsistent with the new item difficulties. Implications of these results for the Bookmark standard setting method are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.358 | 0.716 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".