Metacognitive Strategy Training Adds to the Effects of Working Memory Training in Children with Special Educational Needs
Bibliographic record
Abstract
The effects of computer-based Working Memory (WM) training using two training procedures were examined among sixty-four primary-school children with Special Educational Needs (SEN). Measures of general cognitive ability, auditory and visuospatial working memory, arithmetic ability, and reading and writing skills were gathered and analysed. The referred group of SEN children predominantly had lower performance in auditory WM, arithmetics and reading and writing skills. The SEN children within respective school were randomized into either an active WM training group or a control group and ten schools participating in the WM training study were randomized into one of two different training conditions. At five schools the SEN children received regular WM training and at the other five schools the children received WM training with the addition of metacognitive strategy training. Results showed a significant difference in WM performance during training in favor of the metacognitive intervention. Furthermore, transfer effects occurred on visuospatial WM measures at posttest and at 6-month follow-up. Post-hoc tests showed that the effects pertained only to the metacognitive intervention. No transfer to arithmetic or reading and writing skills occurred after training in the two training conditions. Results were discussed in terms of metacognitive factors being important in optimizing performance in WM training, and that such factors should be taken into consideration when designing interventions for children with SEN. It is also suggested that in referral of children with SEN to remediation with WM training the WM profiles should be taken into consideration to a greater degree.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".