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Record W2795373226 · doi:10.14288/cjur.v2i2.189384

The effectiveness of the Wilson Reading System on multiple measures of literacy for a braille-reading student with a language-based learning disability

2017· article· en· W2795373226 on OpenAlexaff
Emily Van Gaasbeek

Bibliographic record

VenueOpen Collections · 2017
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFluencyBrailleReading (process)Reading comprehensionLiteracyPsychologyCognitive psychologyComputer scienceMathematics educationLinguisticsPedagogy

Abstract

fetched live from OpenAlex

Visually driven and braille driven reading share common language attributes, yet the skills, cognitive load, and sensory system processing required to perform either task are dramatically different. Despite the significant differences in the processes of braille and print reading, traditional approaches to developing braille literacy have primarily relied upon adaptations of approaches used to establish sighted literacy. Furthermore, little research has investigated the effectiveness of these strategies for students who read braille. The outcomes, however, are clear; braille readers struggle to develop effective decoding and fluency skills. One promising literacy program, called the Wilson Reading System (WRS), has been adapted for braille literacy development because it emphasizes fluency and comprehension. Recent research suggests positive qualitative outcomes using the WRS for students with visual impairments. However, no quantitative changes in braille decoding and fluency using the WRS have been established. This study extends previous findings to assess the effectiveness of the WRS on decoding ability, comprehension, oral fluency rate, and reading motivation in a braille-reading student with a language-based learning disability. Results demonstrate an increase in decoding ability, comprehension, reading motivation, but no sign of improvement in oral reading fluency.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.027
GPT teacher head0.354
Teacher spread0.327 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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