Examining Position Effects on Students’ Ability and Test-Taking Speed in the TIMSS 2019 Problem-Solving and Inquiry Tasks: A Structural Equation Modeling Approach
Bibliographic record
Abstract
Position effects occur when changes in item positions on a test impact the test outcomes (e.g., item parameters or test scores). Previous studies found that position effects may vary by the testing context and conditions and thus affect each test-taker differently. With the increasing adoption of digital assessments involving innovative item types that are focused on problem-solving skills, it is also essential to study position effects within this context. This study incorporates item-level scores and screen-level response time data from the Trends in International Mathematics and Science Study (TIMSS) 2019 problem-solving and inquiry tasks for Grade 4 students to examine position effects on students’ ability and test-taking speed. This dataset included responses from 27,682 students from 36 countries. A structural equation modeling approach was employed to model ability and test-taking speed within the same model. The results indicated a small but statistically significant booklet effect on students’ overall mathematics and science ability. The impact of block position changes within the booklets seemed to be greater than the impact of a reordering of subjects tested in the two sessions. The results also showed that when an item block was placed earlier in a test session, students spent more time on the items and performed better. The implications of these findings are discussed.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".