Mathematical Abilities in Children With Developmental Language Disorder
Bibliographic record
Abstract
Purpose This review article provides a scoping review of the literature on mathematical abilities in developmental language disorder (DLD). Children with DLD typically struggle with learning in school; however, the mechanism by which DLD impacts academic success is unclear. Mathematics involves demands in the multiple domains and therefore holds potential for examining the relationship between language and academic performance on tasks mediated by verbal and nonverbal demands. Method A scoping review was performed via computerized database searching to examine literature on mathematics and DLD. The 21 review articles meeting inclusion criteria compared children with typical development or DLD on various tasks measuring numerical cognition. Results Children with DLD consistently performed below peers with typical development on number transcoding, counting, arithmetic, and story problem tasks. However, performance was similar to peers with typical development on most number line, magnitude comparison, and conceptual mathematics tasks. Conclusions The findings suggest a relationship between DLD and mathematics was characterized by more detrimental performance on tasks with higher verbal demands. Results are discussed with respect to typical academic curricula and demonstrate a need for early identification and intervention in DLD to optimize academic outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 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".