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Record W3048824419 · doi:10.3968/11661

Educating Students with Visual Disability in the State of Kuwait: Literature Review and Recommendations

2020· article· en· W3048824419 on OpenAlexvenueno aff
Ibrahim El-Zraigat, Mubarak Alshammari

Bibliographic record

VenueHigher education of social science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsVisual impairmentVariety (cybernetics)PsychosocialPsychologySpecial educationSpecial needsInclusion (mineral)Medical educationNonverbal communicationProcess (computing)Special educational needsMathematics educationDevelopmental psychologyComputer scienceMedicineSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

The primary purpose of this study was to review the educational process of student with visual disability in Kuwait. The visual impairment is one of the special education classes that attracted the attention and attention of researchers and educators. Students with visual impairment show various educational and psychological needs, and the assessment determines the nature of these special needs. Meeting these needs requires specialized educational programs and a variety of services to achieve their maximum potential optimal level of adjustment. The present study is considered a theoretical study. The review indicated that students with visual disability have a wide range of characteristics and specialized needs. Visual disability negatively affected students’ especially academic achievement, nonverbal communication, as well as psychosocial development. These needs require special services in order to meet them. The study ended by offering a number of conclusions and recommendations for better education of this group of disability. Basically, there is a need to rethink with the educational procedures used and develop their education.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.035
GPT teacher head0.397
Teacher spread0.362 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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