Social Media and Language Learning: How: EFL Students Use Online Platforms for Language Learning at the College of Basic Education in Kuwait
Bibliographic record
Abstract
This study explores the role of social media platforms in facilitating both academic and social interactions among English as a Foreign Language (EFL) students at the College of Basic Education in Kuwait. The main aim of this study is to investigate EFL students’ perceptions of this use of social media and thus to determine how it can be used to facilitate language learning. A qualitative analysis approach was used, based on semi-structured interviews with 60 college students, to explore how these learners use platforms such as Facebook, X (formerly Twitter) Instagram, and WhatsApp as tools for language learning. Understanding the use of social media for language learning has relevance in the modern world in terms of it enriching EFL learners’ experiences by bridging the gap between formal education and practical language use, highlighting the need to integrate these digital tools into language learning. The findings in this case reveal that social media is a significant tool for facilitating language learning practice, peer collaboration, and access to educational resources, acting as a critical tool for language learning by offering students opportunities to engage in authentic communication, access to diverse linguistic resources, and chances to participate in online communities that foster collaborative learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".