Working Memory Training and the Effect on Mathematical Achievement in Children with Attention Deficits and Special Needs
Bibliographic record
Abstract
Working Memory (WM) has a central role in learning. It is suggested to be malleable and is considerednecessary for several aspects of mathematical functioning. This study investigated whether work with aninteractive computerised working memory training programme at school could affect the mathematicalperformance of young children. Fifty-seven children with attention deficits participated in an interventionprogramme. The treatment group trained daily, for 30-40 min. at school for five weeks, while the control groupdid not get any extra training. Looking at the group as a whole, mathematical performance improved in thetreatment group compared with the control group directly following the five weeks of training (Time 2), but theresults of the second post-test (Time 3, approximately seven months later) were no longer significant. Since therewas only a small number of girls, the results were analysed for boys only. The boys had improved theirmathematical results in both post-tests. WM-measures improved at Time 2 and 3 relative to Time 1 (pre-test) forthe whole group, and for boys. Differences in training scores were related to differences in the non-verbalWM-measure Span board back.The results indicate that boys aged 9 to 12 with special needs may benefit, over time, from WM training, asshown in the enhanced results in mathematics following WM training. However, as the intervention and controlgroups were not randomised, the results cannot be generalised; the results must be considered with caution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".