Using the Attribute Hierarchy Method to Make Diagnostic Inferences about Examinees’ Cognitive Skills in Critical Reading
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this study is to apply the attribute hierarchy method (AHM) to a subset of SAT critical reading items and illustrate how the method can be used to promote cognitive diagnostic inferences. The AHM is a psychometric procedure for classifying examinees’ test item responses into a set of attribute mastery patterns associated with different components from a cognitive model. The study was conducted in two steps. In step 1, three cognitive models were developed by reviewing selected literature in reading comprehension as well as research related to SAT Critical Reading. Then, the cognitive models were validated by having a sample of students think aloud as they solved each item. In step 2, psychometric analyses were conducted on the SAT critical reading cognitive models by evaluating the model-data fit between the expected and observed response patterns produced from two random samples of 2,000 examinees who wrote the items. The model that provided best data-model fit was then used to calculate attribute probabilities for 15 examinees to illustrate our diagnostic testing procedure.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.553 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it