The analysis of TIMSS 2015 data with confirmatory mixture item response theory: A multidimensional approach
Why this work is in the frame
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Bibliographic record
Abstract
In this study, we illustrated an application of the confirmatory mixture IRT model for multidimensional tests. We aimed to examine the differences in student performance by domains with a confirmatory mixture IRT modeling approach. A three-dimensional and three-class model was analyzed by assuming content domains as dimensions and cognitive domains as item groups. We estimated the item performance differences among the students through structural parameters. There were 463 students from Turkey and 880 students from Canada who participated in the TIMSS 2015 4th-grade mathematics assessment. Results for Turkey indicated, students in Class 2 had better performance in knowing and reasoning compared to those in Classes 1 and 3. Students in Class 2 and Class 3 were similar in applying math concepts compared to students in Class 1. For the Canadian sample, students in Class 2 had better performance in knowing, applying, and reasoning compared to those in Class 1 and 3. Also, Class 3 students were better at applying domain than Class 1. Also, mean values were obtained for all content domains in the two countries. Confirmatory mixture IRT modeling approaches appear to differentiate students’ mathematics competencies.
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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 | 0.038 | 0.306 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 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