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Record W2996453622 · doi:10.5539/res.v12n1p12

Evaluating Children’s Websites in Arabic language

2019· article· en· W2996453622 on OpenAlexvenueno aff
Nahla M. Gahwaji

Bibliographic record

VenueReview of European Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsInteractivityArabicChecklistPsychologyThe InternetAnimationStrengths and weaknessesSample (material)Computer scienceMathematics educationMultimediaWorld Wide WebLinguisticsSocial psychology

Abstract

fetched live from OpenAlex

The Internet is one of the most successful means of providing a rich learning environment, and children are among the most affected by that interactive atmosphere. The research adopted a descriptive approach using an evaluation card in a checklist form to evaluate general, educational content, and technical elements of Arabic- language children’s websites. The evaluation card included (17) domains and (127) items examining (20) Arabic websites for children representing the research sample. In terms of general elements, the research findings confirmed that accessibility and ease of use received the highest rating (93.33%), while continuous timeliness was the lowest (21.25%). In the elements of educational content, the written text was the highest (91.11%), while the interactivity was only (30.77%), and finally the technical elements, the written text at the highest ratings (93%), compared to video, animation and sound at the lowest rating (60.83%). Regarding the availability of key domains in the websites, technical elements received the highest percentage (71%), followed by general elements (62.58%), and the educational content elements were last (53.93%). The research main recommendations involved designing Arabic websites for children with their interests and developmental needs as well as benefiting from websites in the international settings, by avoiding the shortcomings and weaknesses revealed in the research results.

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)0.001
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 score0.381
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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)0.0000.001

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 head0.066
GPT teacher head0.414
Teacher spread0.348 · 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
Published2019
Admission routes1
Has abstractyes

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