The Influence of Equation Alignment on Mental Addition of Children with an Arithmetic Disability
Bibliographic record
Abstract
The purpose of the present study was to investigate whether there is an observable effect of equation alignment on mental addition accuracy and fluency for children with an arithmetic disability ( n = 46) and children with a comorbid arithmetic disability and reading disability ( n = 25). Two objectives were of central importance. First, do children with an AD or show impairments in mental addition accuracy and fluency across different equation alignments (i.e., horizontal and vertical)? Second, do TA children ( n = 47) and children with an AD benefit-greater accuracy and greater fluency-from different equation alignments when performing mental addition? Results suggested that there are notable between groups and little within group differences in equations presented horizontally and vertically. This was most pronounced in the fluency differences between TA children and both AD groups, with very large magnitudes of impairment. Only AD/RD children showed an impairment in accuracy, with poorer performance on equations aligned horizontally, suggesting that this group’s reading impairment might underlay this difficulty. Only TA children’s fluency benefited from equation alignment, with a very large magnitude of improvement when equations were presented vertically.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".