A Critical Analysis of the Body of Work Method for Setting Cut-Scores
Bibliographic record
Abstract
The recent increase in the use of constructed-response items in educational assessment and the dissatisfaction with the nature of the decision that the judges must make using traditional standard-setting methods created a need to develop new and effective standard setting procedures for tests that include both multiple-choice and constructed-response items. The Body of Work (BoW) method is an examinee-centered method for setting cut-scores that applies a holistic approach to student work in order to estimate the cut-scores that differentiate examinees according to their level of performance in situations where both item formats are used. A detailed review of Version 1 and the recent modification, Version 2, are first presented followed by a critical evaluation of the two versions in terms of Berk’s (1986) 10 criteria for defensibility. The results reveal that the BoW method appears to be a promising method for setting cut-scores that could be used on a wider scale in Canada. However, as with other methods, the experience gained from using the BoW method in the field will probably lead to further modifications in an attempt to increase efficiency without sacrificing accuracy.
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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 | 0.235 | 0.506 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".