A Study on the Rationale of Social Media Use by the Students of King Khalid University
Bibliographic record
Abstract
s not it deplorable that in a country that tops in the entire world in using several social media sites does not utilize the same media in acquiring knowledge and skills? In Saudi Arabia, undergraduate students spend a significant amount of time on social media every day, but they are reluctant (or not motivated enough) to use the same media for educational purposes. This study was carried out on the undergraduate English majors of King Khalid University in Muhayil Asir in Saudi Arabia. In the English department, every student carries at least one smart phone with Internet connection, and they are found occupied with their phones on the campus, sometimes even in classrooms, but they are weak both in subject knowledge and skills of English language. The teachers-cum-researchers were baffled with students’ competence because regular users of Internet and social media are supposed to be updated with the subject knowledge as well as confident in using English language. The researchers designed an empirical study to explore students’ rationale of using the social media and their language preference. The study concludes with gloomy findings that students use the media mainly for entertainment and ineffective communication in English language. The worst fact is: they are not motivated enough to use the social media for educational purposes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".