Bibliographic record
Abstract
The rapid growth of social media has significantly shaped education, particularly in language learning across Saudi Arabia. This study examines how platforms such as TikTok, Instagram, and X (formerly Twitter) are being integrated into English language teaching. Using a mixed-methods approach, data were collected through surveys from 150 university students, interviews with 10 English instructors, and content analysis of some followed English-learning accounts on these platforms. Findings showed that around 75% of students use TikTok for learning English, mainly to improve vocabulary and pronunciation, while 65% rely on Instagram for similar purposes. Instructors see the potential of these platforms to enhance student engagement but also express concerns about cultural appropriateness, digital distractions, and the lack of institutional support. Content analysis showed that successful educational accounts attract higher engagement when they include interactive features, visually rich content, and culturally relevant topics. Despite these benefits, challenges remain, such as unequal access to devices and limited digital literacy. While some studies have examined social media in education, there is a noticeable gap in research focusing on its practical use in English language instruction within the Saudi Arabian context. In line with Saudi Arabia’s Vision 2030, which prioritizes English proficiency, this study concludes that social media can serve as a useful supplement to traditional language instruction. It also provides recommendations for educators and content creators to better integrate these tools into English education while addressing current limitations.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".