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Record W2592041494 · doi:10.5539/ijel.v7n3p171

The Effect of Using Computer Program on Developing Verbal Communication among Mentally Retarded Children in the Elementary Stage in Rafha Province

2017· article· en· W2592041494 on OpenAlexvenueno aff
Essam Abdou Ahmed Saleh, Khaled Ahmed Mahmoud Attia, Alaa Ahmed Hassan Al-Jundi

Bibliographic record

VenueInternational Journal of English Linguistics · 2017
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Health Research
Canadian institutionsnot available
FundersNorthern Border University
KeywordsMentally retardedSession (web analytics)PsychologySample (material)Nonverbal communicationMathematics educationSignificant differenceScale (ratio)Educational programControl (management)Developmental psychologyComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

This study aims at investigating the effect of using a computer program on developing verbal communication among the educable mentally retarded students in the elementary stage in Rafha Province. The sample was selected from the students of the intellectual education classes in the elementary stage in Rafha province/ Northern Borders Region. The researchers followed the experimental method to conduct the study. The sample consisted of 20 students randomly distributed into two groups: an experimental group that was taught using the suggested verbal communication developing computer program and a control group that did not receive any educational program. The program was made up of 21 sessions. Each session lasted for 45 minutes, three times a week. The researchers used verbal communication scale to collect data and to compare the performance of the experimental and control groups in the pre and post tests. The results of the study indicated that there are statistically significant differences on the verbal communication scale among the students of the two groups at the level of (0.01) in favor of the experimental group.

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)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.010
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 science0.0010.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.477
Teacher spread0.417 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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