Analyzing the Effects of Test, Student, and School Predictors on Science Achievement: An Explanatory IRT Modeling Approach
Bibliographic record
Abstract
This study aimed to investigate the impact of item features (i.e., content domain), student characteristics (i.e., gender), and school variables (i.e., school type) on students’ responses to a nationwide, large-scale assessment in Turkey. The sample consisted of 7507 students who participated in the 2016 administration of the Transition from Primary to Secondary Education Exam (TPSEE, referred to as “TEOG” in Turkey). Explanatory item response modeling was used for analyzing the effects of content domain, gender, school type, and their interactions on students’ responses to the science items on the exam. Five explanatory models were constructed to examine the effects of the item, student, and school variables sequentially. Results indicated that female students were more likely to answer the items correctly than male students. Also, students from private schools performed better than students from public schools. In terms of content, the biology items appeared to be significantly easier than the physics items. All interactions between the predictors were significant except for the Gender x School Type and Content x Gender x School Type interactions. The interactions between the predictors suggested that test developers, teachers, and stakeholders should be aware of potential item-level bias occurring in the science items due to complex interactions among the items, students, and schools characteristics.
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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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".