Adopting Social Networking Sites (SNSs) as Interactive Communities among English Foreign Language (EFL) Learners in Writing: Opportunities and Challenges
Bibliographic record
Abstract
As most traditional classroom environments in English as Foreign Language (EFL) still restrict learners’ collaboration and interaction in college writing classes, today, the majority of EFL learners are accessing Social Networking Sites (SNSs) as online communities of practice (CoPs) for adopting informal collaborative learning as a way of practicing English beyond the classroom. This study aimed to investigate the opportunities and challenges of SNSs as learning environment in writing in English. The study was conducted among 24 active and regular EFL learners joining the Only for English Learning Facebook (FB) CoP – a group developed and maintained by a few instructors in English – for EFL learners coming from different EFL Arab countries. The data was collected from the learners’ interactional exchanges in the weekly posted writing activities as well as their responses to online open questions posted by the instructor. Based on the mixed analysis of the data, the quantity of the EFL learners’ participation in the writing activities highly increased in the second session. Moreover, the learners were motivated to generate ideas, write their paragraphs and scaffold each other in paragraph writing. The findings also revealed that the EFL participants perceived this CoP as an interactive learning environment that contributed to enhancing their writing by engaging in learner-learner and learner-instructor interaction, information sharing, communicating and socializing with friends and developing a sense of belonging. However, a few challenges faced by the participants in such an online CoP were identified by the participants, and therefore, some valuable assistive features are suggested to be involved in the FB CoP for achieving further EFL development in the future.
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 | 0.005 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".