Perceptions of Using Social Media as an ELT Tool among EFL Teachers in the Saudi Context
Bibliographic record
Abstract
Social media technologies have undeniably become an integral part of people’s lives and they have been widely used amonsgest the new genrations, particularly, university students. This widespread of social media technologies has certainly made a huge impact on the way people learn and interact with each other resulting in the emergence of communities of learning that are supported by collective intelligence. This study is based on quantitative methods using a survey instrument to gather descriptive data regarding the perceptions of seventy-five (n=75) randomly chosen male and female English as a Foreign Language (EFL) teachers at two Saudi tertiary institutions. The study utilized a 14 Likert scale statements where each statement had five Likert-type items for the participants to choose from. Analysis of the gathered data indicated that the majority of the participants believe strongly in the pedagoocal values and benefits of using social media as an ELT tool in the EFL classes in the Saudi context. Nevertheless, the majority expressed reservations with regards to the extent to which social media can be freely allowed to be used in the EFL classroom where they perceive it as having a double edged sword effect and that is mainly due to some undesired distractions that some students may resort to which may occasionaly result in the opposite of the intended effect of their usage. The study recommends more research studies in this area so as to closely understand how experienced EFL teachers utilize social media in their classes in order to develop best practices for implementing social media in teaching and learning in EFL in the Saudi contexts
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".