Entraînement de la mémoire de travail : Effets sur la performance en mathématiques.
Bibliographic record
Abstract
The main components of working memory (WM; Baddeley & Hitch, 1974), central executive, phonological loop and visuospatial sketchpad, are related to mathematics skills (Friso-van den Bos, Van der Ven, Kroesbergen, & Van Luit, 2013). Different studies have shown that WM training can increase WM capacity (Randall & Tyldesley, 2016). In that context, this research seeks to verify the effects of WM training on the components of WM as well as on performance in arithmetic and in problem solving among students in the first grades of primary school (6 to 8 years of age). The project also seeks to verify whether improvements are maintained over a six-month period and whether the training has a differential effect in mathematics depending on initial WM capacities. The results of this randomized and controlled study indicate that only central executive capacities are improved by WM training. Gains in verbal modality tend to be maintained over six months while visuo-spatial improvement does not. There was no observable effect on mathematic skills. However, WM training has a differential effect on problem solving, since children who had low initial WM performance did improve their problem solving performance. In conclusion, the effects of WM training are specific, generally to the central executive, and differentially to problem solving skills among those students with lower WM capacities. (PsycINFO Database Record
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".