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Record W3006060234 · doi:10.47577/tssj.v3i1.86

Developing Image Reading Skills to Support Visual Learning for Children with Learning Disabilities

2020· article· en· W3006060234 on OpenAlexaff
Magda Saleh, Marwa Battisha

Bibliographic record

VenueTechnium Social Sciences Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Accessibility for Disabilities
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsReading (process)Learning disabilityPsychologyControl (management)Test (biology)Visual learningGroup learningMathematics educationDevelopmental psychologyCognitive psychologyComputer scienceArtificial intelligenceLinguistics

Abstract

fetched live from OpenAlex

The training activity is adapted to learn the child with learning difficulties in how to read an image in the right way, which in turn develops his visual learning. Two groups are adopted: ten children with learning difficulties as a control group, and ten others as an experimental group, on which the authors have applied specialized-training activities for learning children with learning difficulties in reading images. A test has been applied to evaluate the visual learning of children who have learning difficulties on both the control and experimental groups. It has been shown that there are statistically significant differences in the favor of the experimental group among the average ranks of the control group scores.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.378
Teacher spread0.337 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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Same venueTechnium Social Sciences JournalSame topicDigital Accessibility for DisabilitiesFrench-language works237,207