Stability of Computational Deficits in Math Learning Disability From Second Through Fifth Grades
Bibliographic record
Abstract
This study utilized growth modeling to investigate the stability of two computational deficits that are strongly associated with math learning disability (MLD)-procedural deficits and fact fluency deficits. Math "deficit" was defined in two ways-having scores in the lower 10th percentile (MLD) and between the 11th and 25th percentiles (LA, low achieving). The longitudinal sample was composed of 214 children. Between the second and fifth grades, children who started at grade 2 with procedural deficits showed more rapid growth in procedural skills than typically achieving (TA) children. The procedural deficits group was able to narrow the initial gap in procedural skills and catch up with their normal peers. In contrast, children who started at grade 2 with fact fluency deficits showed the same growth in fact fluency skills as TA children. The fact fluency deficits group maintained their initial gap and was not able to catch up. This pattern of results held for both the MLD and LA criteria. Fact fluency deficits are more stable in the elementary years. Further results showed that fact fluency deficits may be associated with more pervasive and persistent cognitive deficits than procedural deficits. The fact fluency group showed persistent cognitive deficits in working memory, processing speed, and phonological processing, from grades 2-5. The group with procedural deficits at grade 2 showed persistent deficits in working memory and processing speed, but closed the gap in phonological skills. In addition, only the MLD procedural deficits group showed these cognitive deficits; the LA group was no different than the typical achievers. The present results support the hypothesis that fact fluency deficits and procedural deficits are dissociable and may represent distinct subgroups of MLD.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".