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Record W2042025648 · doi:10.1080/1350293x.2015.1016802

Improving cognitive processes in preschool children: the COGEST programme

2015· article· en· W2042025648 on OpenAlexaff
Sílvia Mayoral-Rodríguez, Federico Pérez Álvarez, J. P. Das

Bibliographic record

VenueEuropean Early Childhood Education Research Journal · 2015
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCognitionPsychologyReading (process)Developmental psychologyCognitive developmentTreatment and control groupsCognitive trainingLanguage developmentControl (management)Cognitive psychologyComputer scienceLinguisticsMedicine

Abstract

fetched live from OpenAlex

The present study provides empirical evidence to support the hypothesis that pre-school children's cognitive functions can be developed by virtue of a training tool named COGENT (Cognitive Enhancement Training). We assumed that COGENT (COGEST in Spain) which is embedded in speech and language, will enhance the core cognitive processes that are required for reading acquisition. The participants included 97 students aged four and five, who received COGEST for six months. The two core cognitive processes targeted were simultaneous and successive. These were measured before and after the application of COGEST. Simultaneously, we assessed a control group in the same manner, but it did not receive any COGEST training. Results showed that the COGEST group improved its simultaneous and successive processing performance significantly (p < 0.00) with Cohen effect size moderate for simultaneous and small for successive, whereas the control group displayed no such change. The study provides evidence for the effectiveness of COGEST in preschoolers to improve the simultaneous and successive cognitive processes (PASS) involved in reading acquisition and suggests its use as a training programme may help prevent reading difficulties.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.048
GPT teacher head0.357
Teacher spread0.309 · 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 teacher head, not a consensus.

Study designObservational
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

Citations11
Published2015
Admission routes1
Has abstractyes

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