Examining the role of the visuospatial sketchpad in children's math calculation skills using Baddeley and Hitch's model of working memory
Bibliographic record
Abstract
Math difficulties (MDs) occur in about 3-7 % of children and have been associated with academic, health, and occupational challenges. To date, findings about the role of working memory in MDs have been conflicting. The Automated Working Memory Assessment Battery (AWMA), which assesses all components of Baddeley and Hitch's model of working memory, was used to investigate which component of the model was most related to math calculation skills in elementary-school children. Participants were 94 (52 male) children (M age = 9 years 1 month; Range = 6 years 0 months to 11 years 8 months). As hypothesized, math calculation scores were correlated with all four working memory components (phonological loop, visuospatial sketchpad, verbal and visuospatial central executive). After accounting for age, phonological processing, and attention, the visuospatial sketchpad was the only memory component that contributed to the prediction of math calculation scores, explaining an additional 10.2 % of unique variance. Short-term visuospatial memory should be assessed in children having difficulty with math and children could benefit from interventions that include attention to the development of both visuospatial memory and math calculation skills. This study did not use a longitudinal design and so we cannot conclude that weak visuospatial memory impedes the development of math calculation skills. Future research should use longitudinal designs and investigate other types of math skills.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".