Using the Attribute Hierarchy Method to Make Diagnostic Inferences About Examinees' Cognitive Skills
Bibliographic record
Abstract
INTRODUCTION Many educational assessments are based on cognitive problem-solving tasks. Cognitive diagnostic assessments are designed to model examinees' cognitive performances on these tasks and yield specific information about their problem-solving strengths and weaknesses. Although most psychometric models are based on latent trait theories, a cognitive diagnostic assessment requires a cognitive information processing approach to model the psychology of test performance because the score inference is specifically targeted to examinees' cognitive skills. Latent trait theories posit that a small number of stable underlying characteristics or traits can be used to explain test performance. Individual differences on these traits account for variation in performance over a range of testing situations (Messick, 1989). Trait performance is often used to classify or rank examinees because these traits are specified at a large grain size and are deemed to be stable over time. Cognitive information processing theories require a much deeper understanding of trait performance, where the psychological features of how a trait can produce a performance become the focus of inquiry (cf. Anderson et al., 2004). With a cognitive approach, problem solving is assumed to require the processing of information using relevant sequences of operations. Examinees are expected to differ in the knowledge they possess and the processes they apply, thereby producing response variability in each test-taking situation. Because these knowledge structures and processing skills are specified at a small grain size and are expected to vary among examinees within any testing situation, cognitive theories and models can be used to understand and evaluate specific cognitive skills that affect test performance.
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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 | 0.018 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".