Creating a test blueprint for a progress testing program: A paired-comparisons approach
Bibliographic record
Abstract
CONTEXT: Creating a new testing program requires the development of a test blueprint that will determine how the items on each test form are distributed across possible content areas and practice domains. To achieve validity, categories of a blueprint are typically based on the judgments of content experts. How experts judgments are elicited and combined is important to the quality of resulting test blueprints. METHODS: Content experts in dentistry participated in a day-long faculty-wide workshop to discuss, refine, and confirm the categories and their relative weights. After reaching agreement on categories and their definitions, experts judged the relative importance between category pairs, registering their judgments anonymously using iClicker, an audience response system. Judgments were combined in two ways: a simple calculation that could be performed during the workshop and a multidimensional scaling of the judgments performed later. RESULTS: Content experts were able to produce a set of relative weights using this approach. The multidimensional scaling yielded a three-dimensional model with the potential to provide deeper insights into the basis of the experts' judgments. CONCLUSION: The approach developed and demonstrated in this study can be applied across academic disciplines to elicit and combine content experts judgments for the development of test blueprints.
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 | 0.166 | 0.369 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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; 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".