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Intervenção precoce nas dificuldades de aprendizagem da leitura com recurso ao software Graphogame1

2016· article· pt· W2569137418 on OpenAlexfundno aff
Ana Sucena, Ana Filipa Silva, Fernanda Leopoldina Viana

Bibliographic record

VenueLetrônica · 2016
Typearticle
Languagept
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
FundersEuropean Regional Development FundFundação para a Ciência e a TecnologiaInstituto Politécnico do PortoInternational Council for Canadian StudiesBanco Santander
KeywordsHumanitiesMedicinePsychologyPhilosophy

Abstract

fetched live from OpenAlex

Neste estudo avaliou-se o impacto de um software de apoio às dificuldades de aprendizagem da leitura – o Graphogame – ao nível das relações letra-som, consciência fonémica, leitura de palavras e leitura de pseudopalavras. Os participantes eram 57 crianças, a frequentar o 1º ano de escolaridade, falantes nativos do português europeu, identificadas como em risco de virem a apresentar dificuldades de aprendizagem da leitura. As crianças foram divididas em três grupos: (a) intervenção com recurso a software – treino com software Graphogame, (b) intervenção mista – treino com recurso ao software Graphogame e a sessões de competências pré-leitoras e leitoras orientadas por técnico de educação e (c) ausência de intervenção para lá daquela prevista no sistema regular de ensino. Os resultados revelam efeitos positivos de ambos os tipos de intervenção em relação ao grupo controlo. Revelam ainda um efeito mais pronunciado para o grupo de intervenção mista do que para o grupo de intervenção com recurso exclusivo ao software.

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)0.005
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.035
GPT teacher head0.319
Teacher spread0.285 · 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 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

Citations13
Published2016
Admission routes1
Has abstractyes

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