Exploring EFL Teachers’ Perspectives on the Role of Social Media for Building Trust in the Workplace
Bibliographic record
Abstract
The role of social media in the educational landscape has evolved significantly during the past two decades, particularly amid the COVID-19 pandemic. However, a significant gap exists in understanding the impact of social media on developing trust among English as Foreign Language (EFL) teachers in their professional environments. Therefore, this qualitative study investigated the perceptions of experienced EFL teachers regarding using various social media platforms to cultivate trust in the workplace. The study employed a qualitative research approach, utilizing semi structured interviews as the primary data collection method. Interviewees include fourteen experienced EFL teachers from the Saudi universities. Thematic content analysis was conducted using Nvivo software to analyze the transcribed data. The findings revealed seven main themes with corresponding subthemes: enhancing collaboration, building trust through emotional connections, concerns about privacy and trust, professional growth and development, nurturing trust through positive online interactions, motivating peers, and fostering goal achievement. The findings demonstrated that social media platforms are critical in enhancing collaboration and trust among EFL teachers in the workplace. The implications of social media usage on trust development among experienced EFL teachers have been illustrated.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".