Testing Expert-Based Versus Student-Based Cognitive Models for a Grade 3 Diagnostic Mathematics Assessment
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 was to evaluate the adequacy of three cognitive models, one developed by content experts and two generated from student verbal reports for explaining examinee performance on a grade 3 diagnostic mathematics test. For this study, the items were developed to directly measure the attributes in the cognitive model. The performance of each cognitive model was evaluated by examining its fit to different data samples: verbal report, total, high-, moderate-, and low ability using the Hierarchy Consistency Index (Cui & Leighton, 2009), a model-data fit index. This study utilized cognitive diagnostic assessments developed under the framework of construct-centered test design and analyzed using the Attribute Hierarchy Method (Gierl, Wang, & Zhou, 2008; Leighton, Gierl, & Hunka, 2004). Both the expert-based and the student-based cognitive models provided excellent fit to the verbal report and high ability samples, but moderate to poor fit to the total, moderate and low ability samples. Implications for cognitive model development for cognitive diagnostic assessment are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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