Social media in language learning: a mixed-methods investigation of students’ perceptions
Bibliographic record
Abstract
The literature on students’ perceptions towards using Social Media (SM) for language learning reports mixed findings: while some studies indicate learners’ positive perceptions of their use for academic purposes (e.g., Bani-Hani et al.), others suggest that learners’ perceptions might vary due to their proficiency in the language (e.g., Gamble & Wilkins). There is also evidence that students’ do not always wish to share their SM environments for educational purposes). This study investigates students’ attitudes towards the use of four popular SMs (WhatsApp, Snapchat, Instagram and Twitter) in learning English as a foreign language.Ninety-nine adult English learners at a university in Saudi Arabia, active users of SM, participated in this mixed-methods study, which consisted of individual surveys and interviews. A two-way analysis of variance revealed that there are differences between beginner and advanced students in their perceptions of the usefulness of SM applications for language learning, but not in their affective feelings towards SM use outside the classroom, nor their choice of SM application for learning. Frequency counts indicated that the groups’ choices of SM varied according to different language purposes and the skills to be learned (e.g., they preferred WhatsApp for communication with family and friends, Twitter for reading, and Snapchat for learning aural skills). Further qualitative analysis revealed that advanced learners were more reluctant to using SM for academic 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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".