The Influence of Social Media on Improving the Pragmatic Skills of EFL Saudi University Students
Bibliographic record
Abstract
Social media platforms can be a gateway through which English as a foreign language (EFL) students gain access to the world of the target language, communicate with native speakers, and learn more about their culture. Against this background, the present study aims to investigate whether EFL students’ use of social media has impacted the development of their pragmatic competence and conversational skills. Sixty Saudi EFL students, aged 17 or older, studying in the English Language and Translation Department at the Saudi Electronic University participated in the study. Data were collected through an online questionnaire that assessed the participants’ perceptions of using social media networks to practice their English conversation skills. The results showed that social media use had a positive impact on the students’ pragmatic and conversational skills. Specifically, a significant proportion of the participants reported an awareness of conversational and pragmatic improvements resulting from social media use that may not be available in traditional classroom settings. These findings suggest that technology-assisted language learning, particularly learning using social media, should be incorporated into language instruction to improve EFL students’ pragmatic and conversational knowledge. In this regard, social media networks are especially valuable in providing authentic and meaningful language and interaction opportunities. Future research could examine other forms of technology-assisted language learning beyond social media and employ a mixed-methods approach to gain a more comprehensive understanding of the impact of mobile-assisted language learning (MALL) on EFL learners’ pragmatic and conversational proficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".