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Record W3165318758 · doi:10.1177/215416472005500405

Effects of Three Combined Reading Instruction Devices on the Reading Achievement of Adolescents with Mild Intellectual Disability

2020· article· en· W3165318758 on OpenAlexaff
Céline Chatenoud, Catherine Turcotte, Rebeca Aldama

Bibliographic record

VenueEducation and training in autism and developmental disabilities · 2020
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPhonicsReading (process)Reading comprehensionPsychologyContext (archaeology)ComprehensionMathematics educationMeaning (existential)Intervention (counseling)Learning disabilityAcademic achievementPedagogyDevelopmental psychologyComputer sciencePrimary educationLinguistics

Abstract

fetched live from OpenAlex

Upon entering high school, students with ID who may be able to read simple texts often have difficulty grasping meaning when required to understand more complex texts. This failure affects their overall academic performance, since at this age, it is no longer just a question of learning to read, but rather of reading to learn, in all disciplines. To date, only a very small number of studies have described the types of instruction essential to promote reading comprehension among adolescents with ID; none provide teachers with guidance on how to implement optimal evidence-based instruction. This paper presents the results of an intervention design combining three reading instruction devices with regard to the reading achievement of students with MID. Developed through a collaborative research project, this approach showed promise when used in an experimental context. While outcomes did not quite correspond to what was expected in terms of significant improvement in comprehension, significant effects were observed, especially in relation to phonics and accuracy. Practical implications and recommendations for future research are discussed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.030
GPT teacher head0.277
Teacher spread0.246 · 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.

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

Citations9
Published2020
Admission routes1
Has abstractyes

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