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Record W2177664726 · doi:10.5539/ies.v8n12p167

IPads Enhance Social Interaction Skills among Hearing-Impaired Children of Low Income Families in Saudi Arabia

2015· article· en· W2177664726 on OpenAlexvenueno aff
Raja Omar Bahatheg

Bibliographic record

VenueInternational Education Studies · 2015
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsnot available
FundersResearch Centre for the HumanitiesKing Saud University
KeywordsPsychologySocial skillsHearing impairedClass (philosophy)Social relationDevelopmental psychologyLow incomeSocial changeSocial psychologyAudiologyMedicine

Abstract

fetched live from OpenAlex

<p class="apa">This research tries to investigate the technical contribution on improving the social interaction of hearing-impaired children from low income families in Saudi Arabia. It compares the social interaction skills of hearing-impaired children who do and do not have access to IPads. To achieve the goals of the study; seventeen children aged five-years-old were given access for the first time to iPads to enhance their social skills.</p><p class="apa">The researcher downloaded 10 educational games on to the iPads and gave each family one iPad whom their child was required to play with the iPad for three hours daily. The researcher used the Child’s Social Interaction Scale CSIS as a pre- or post-application measurement to assess the hearing-impaired children’s social interaction skills.</p><p class="apa">Results of the study showed that hearing-impaired children can make all behaviours that are essential to successful social interaction. Also, these children become more sociable, saying thank you, apologising to others, following rules and waiting for a turn. In light of the results, the researcher recommended commercial companies who create games for children to pay attention to hearing-impaired children and develop techniques to help them play with these games in order to develop their interaction social skills alongside normally hearing children.</p>

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 score0.049
Threshold uncertainty score0.564

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 head0.058
GPT teacher head0.430
Teacher spread0.372 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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