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Record W2494044124 · doi:10.5539/ies.v9n8p194

The Role of Social Communication Tools in Education from the Saudi Female Students’ Perceptions

2016· article· en· W2494044124 on OpenAlexvenueno aff
Nawal Hamad Mohamad Aljaad

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsnot available
FundersKing Saud University
KeywordsSocial mediaPerceptionPsychologySpecialtySocial classSociologySocial sciencePublic relationsMedical educationPedagogyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

<p class="apa">This study aims at identifying the role of social communication tools in education from the Saudi female students’ perspectives that are studying at the college of education in King Saud University-Riyadh. This study used a survey, which was distributed to 500 female students. The results showed that 90% of respondents used social media where 95% said social media improved interaction with other and raised the sense of social responsibility, 56% used all tools of social media. 45% used social media more than 6 hours daily. 61% believed that social networks promoted democratic values and spread political culture.62% of respondents used social media to do homework or academic projects and researches. 99% of respondents believed that social media allowed following new information about their academic specialty and obtained specialized scientific consulting. 79% of respondents believed that one goal of creating accounts in social networks were learning specific science knowledge or a foreign language. 9% of respondents benefited from social media in social educational consulting. 44% of respondents preferred to debate in scientific and educational topics. 84% of respondents agreed that social networks provided the opportunity to form relationships with those interested in a particular scientific subject and exchanged experiences and information with them.<strong></strong></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)0.001
metaresearch head score (Gemma)0.002
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 score0.375
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.057
GPT teacher head0.462
Teacher spread0.404 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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