The EFL Learning Process: An Examination of the Potential of Social Media
Bibliographic record
Abstract
Social media as a technological tool has recently come to support learning in both academic and public use. Students typically use social networks to enhance their education by discussing and exchanging academic content. However, its impact that needs carefully study to the vast inroads that social media has made into the academic sphere. Therefore, the purpose of this study is to examine its impact on the process of learning English language in English as a Foreign Language (EFL) contexts. It also aims to determine the impact of employing a social media platform in the Saudi classroom on the learning of EFL students. This study explores the role of social media by giving a panoramic view of the types of social media and social networking sites, the use of social medias in education, social media in learner engagement, social media and students' achievement, social media application in the EFL classroom and finally, social media research in the Saudi higher education scene, and the challenges of each of these. This study concludes that students can benefit the most from these media when they are encouraged to use their mobile devices as learning tools. This conclusion echoes earlier findings in the Saudi context that showed the positive impact of social media applications in boosting students' English language learning. Based on a review of the literature gathered from diverse sources, it is recommended to investigate the inclusion of social media applications, platforms and sites in the English language course descriptions at Saudi universities.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".