The Effect of Removing Examinees with Low Motivation on Item Response Data Calibration
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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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 | 0.232 | 0.582 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".