The Role of Social Communication Tools in Education from the Saudi Female Students’ Perceptions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".