Bring a Foreign Language and Its Cultures to Saudi EFL University-Level Classrooms
Bibliographic record
Abstract
This study aims to investigate the effects Twitter has as a social networking platform on the development of Saudi EFL psychological variables (attitude, confidence, motivation, interest in L2 culture, social interaction and engagement), actual learning outcomes and the relationship between these psychological variables and their results. Twitter provides a valued accessible window to the target culture and promotes cross-cultural competence and comprehension that is focused on meaning rather than form, as well as repeated exposure to L2 cultural products, practices, perspectives and the target language. A sample of 39 students enrolled in an English course during the second semester of the 2014-2015 academic year, as well as two non-native English speakers (NNSs) working at the English Program, agreed to participate in the study. It adopts a combined inductive-deductive research approach to fulfil the research purpose and answer the research questions. The findings of this study underscore the latent use of the Twitter microblogging platform in EFL classes, as well as revealing the positive impact upon Saudi EFL students’ social interaction (engagement), enthusiasm and interest in learning more about L2 culture in English language classes.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".