Rumination as a Moderating Effect Between Math Computation and Executive Function Skills in Elementary Students
Bibliographic record
Abstract
Although students with stronger executive functions (EFs) tend to do better on math computation (MC) assessments than students with weaker EFs, stressful testing situations may lower or affect their mathematical ability. Rumination is one maladaptive coping strategy that can negatively affect EF processes, but little is known about how it impacts the relationship between EFs and MC. This study aimed to examine the relationship between students’ performance on a standardized MC task and ratings of EF ability as a function of their level of rumination. In a sample of students from Grades 4 to 6 ( n = 72, mean age = 10.74), there was an interaction between EF scores and rumination in predicting MC. Students with weaker EF scores demonstrated worse math performance than students with stronger EF scores. Interestingly, their level of rumination moderated this association. Specifically, EF difficulties were only associated with less proficient MC performance among high ruminators; this association was not observed among those students reporting low rumination levels. For school psychologists, these findings provide insight into the potential causes of poor MC performance among students with average or better EFs.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".