Validating Student Score Inferences With Person‐Fit Statistic and Verbal Reports: A Person‐Fit Study for Cognitive Diagnostic Assessment
Bibliographic record
Abstract
The goal of this study was to investigate the usefulness of person‐fit analysis in validating student score inferences in a cognitive diagnostic assessment. In this study, a two‐stage procedure was used to evaluate person fit for a diagnostic test in the domain of statistical hypothesis testing. In the first stage, the person‐fit statistic, the hierarchy consistency index (HCI; Cui, 2007 ; Cui & Leighton, 2009 ), was used to identify the misfitting student item‐score vectors. In the second stage, students’ verbal reports were collected to provide additional information about students’ response processes so as to reveal the actual causes of misfits. This two‐stage procedure helped to identify the misfits of item‐score vectors to the cognitive model used in the design and analysis of the diagnostic test, and to discover the reasons of misfits so that students’ problem‐solving strategies were better understood and their performances were interpreted in a more meaningful way.
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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 | 0.083 | 0.295 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".