The Effect of Removing Examinees with Low Motivation on Item Response Data Calibration
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Bibliographic record
Abstract
The purpose of this study was to evaluate the effect of removing examinees with low motivation on the estimates of test-item parameters when using an item response model (IRM) for large-scale assessment (LSA) data. This study was conducted using a Grade-9 LSA of mathematics. Current IRMs do not flag or filter the effect of low motivation on the estimates of test item parameters data calibrations used to assess examinee abilities and design exams for LSA. The effect of low motivation may pose a threat to the validity of test data interpretations. Motivation, as defined by expectancy-value and self-efficacy theory, was identified from self report data using a principal component analysis (PCA). The PCA scores were used to create two groups of examinees with high and low motivation to examine the effect of removing examinees with low motivation on the estimates of test item parameters when comparing a standard 3-parameter logistic (3PL) IRM to a 3PL low motivation filter IRM. The results suggested that test item parameters seemed to be overestimated under the 3PL IRM when examinees with low motivation were not removed from the test data calibration. The outcome of this study supports the literature and may provide an avenue to flag the effect of low motivation on LSA data analyses.
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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 | 0.009 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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