Online Social Networking in the Teaching of English as a Foreign Language
Bibliographic record
Abstract
The online social networking sites like the Facebook, Google+, Twitter, LinkedIn and WhatsApp are the most widely used platforms for routinely essential communications. However, the role and implications of using online social networking sites in teaching still remain unestablished (Roblyer et al., 2010). In view of the ever-advancing trends in using online social networking, a study of the EFL teachers’ extent of using digital social networking is taken up at the University of Tabuk in Saudi Arabia. A group of English language teachers at the university were consulted for the data required for analysis. The study employed a mixed methods research approach that entailed a survey questionnaire on Likert scale distributed to a sample of teachers. The data obtained from the survey questionnaire were subjected to Cronbach’s alpha test for measuring the internal consistency of the items. After confirming the internal consistency of the items, the same questionnaire was employed for semi-structured face-to-face interviews that included a discussion on the opinions of other respondents and their responses were again registered for comparison. The resultant final data were analyzed qualitatively in light of Jenness’s (1932) conformity theory to establish whether the teachers are comfortably in favor of using online social networking for teaching purposes. And the inferences drawn from the analysis are provided for further insight into the use of social networking in the teaching of English.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".