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Record W3196858744 · doi:10.5296/ijld.v11i3.18985

Impact of Games and Online Activities on Students with Learning Disabilities in Improving Visual Perception and Maintaining Vision Power

2021· article· en· W3196858744 on OpenAlexaff
Yazan Shaher Mahafza

Bibliographic record

VenueInternational Journal of Learning and Development · 2021
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Health Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsPerceptionPsychologyBachelorCurriculumMathematics educationLearning disabilityVariable (mathematics)Bachelor degreeMedical educationPedagogyDevelopmental psychologyGeographyMathematics

Abstract

fetched live from OpenAlex

The study aimed to identify the impact of games and electronic activities in improving visual perception in students with learning disabilities using the descriptive survey curriculum study. The study members are from the entire community of 160 teachers and a teacher of doctors with learning disabilities of education directorates in The Province of Oman. The results showed that the level of impact of games and electronic activities in improving visual perception in students with learning difficulties has come with an average result. The average arithmetic of the total degree (3.66), as well as the results, showed statistically significant differences in the impact of the sex variable and for females, the effect of the variable scientific qualification, and for the benefit of the bachelor. The impact of the variable years of experience and in favor of experience from (5years to 10 years) and the effect of the variable method of teaching came in favor of the electronic method.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.038
GPT teacher head0.493
Teacher spread0.454 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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