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Record W2094774843 · doi:10.12735/ier.v2i2p19

Adaptive Cognitive Training Enhances Executive Control and Visuospatial and Verbal Working Memory in Beginning Readers

2014· article· en· W2094774843 on OpenAlexvenueno aff
Judith G. Foy

Bibliographic record

VenueInternational Education Research · 2014
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsnot available
Fundersnot available
KeywordsWorking memoryCognitive psychologyPsychologyCognitionWorking memory trainingCognitive trainingAttentional controlControl (management)Executive functionsVerbal memoryComputer scienceNeuroscienceArtificial intelligence

Abstract

fetched live from OpenAlex

In this study we examined whether children’s working memory could be enhanced by adaptive cognitive training (ACT) and whether training outcomes would relate to behavioral self-regulation, a measure of executive control (EC) and certain pre-reading outcomes (phoneme awareness and letter knowledge). Children from economically disadvantaged communities were randomly assigned to an ACT (n = 23) or a wait-list control (n = 27) group. ACT consisted of an average of 20 minutes per day of adaptive visuospatial working memory training (Cogmed-JM) for up to 25 days at the beginning of the school year. ACT significantly improved performance in near-transfer (untrained visuospatial test) and far-transfer (tests of verbal working memory and behavioral self-regulation). However, ACT had no direct effects on either measure of pre-reading skill. Our findings suggest that ACT may indirectly help children at risk for later reading problems to benefit from instruction opportunities by developing self-regulation and memory skills.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.132
GPT teacher head0.447
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2014
Admission routes1
Has abstractyes

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