Identifying Potential Test Item Misalignment Using Student Verbal Reports
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 the present investigation was to identify the relationship among different indicators of uncertainty that lead to potential item misalignment. The item-based indicators included ratings of ambiguity and cognitive complexity. The student-based indicators included (a) frequency of cognitive monitoring per item, (b) levels of misinterpretation per item, and (c) levels of lack of confidence per item. Results indicate that item cognitive complexity was related to all student-based indicators even after controlling for students' performance on the item. Moreover, item ambiguity was related to levels of item misinterpretation but not to frequency of student cognitive monitoring or lack of confidence. The implications of these conclusions for identifying item misalignment are discussed in light of construct-relevant and construct-irrelevant sources of ambiguity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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