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Record W2508820519 · doi:10.12735/ier.v4n2p14

Teaching Word Recognition to Children with Intellectual Disabilities

2016· article· en· W2508820519 on OpenAlexvenueno aff
Michael J. Maiorano, Marie Tejero Hughes

Bibliographic record

VenueInternational Education Research · 2016
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsWord (group theory)Word recognitionPsychologyLinguisticsLearning disabilityDevelopmental psychologyReading (process)Philosophy

Abstract

fetched live from OpenAlex

Learning to read affords individuals with intellectual disabilities (ID) a means to function in a literate society. However, one of the most overwhelming challenges for children with ID to accomplish is learning how to read independently. In this study a three-step decoding strategy was used with a constant time delay procedure to teach word reading to children with ID using a phonics-based curriculum. A non-concurrent multiple baseline design with two intervention phases was used to examine the percentage of letter-sounds correctly decoded and the percentage of words read correctly. The findings indicated that all the children learned to read words using the three-step decoding strategy and constant time delay procedure. It was also noted that across all children letter-sound decoding accuracy outpaced word reading accuracy. Although each child made gains in reading words, these gains were not sufficient to infer generalization. These results suggest that the decoding strategy and time delay procedure may be effective at instructing children with ID who are having a difficult time blending sounds together to read word, but additional supports are warranted.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0070.005

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 head0.097
GPT teacher head0.461
Teacher spread0.364 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations2
Published2016
Admission routes1
Has abstractyes

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