An Examination of Code-Switching: Motivational Factors and Patterns among Saudi EFL University Students
Bibliographic record
Abstract
This study investigates the motivations and patterns of code-switching among Saudi EFL (English as a Foreign Language) university students that influence cultural identities on their language practices. The research has used both qualitative and quantitative methods through a set of questionnaire and semi-structured interviews to collect data. The study has exclusively focused female EFL students from the English Language Department at the Applied College of King Khalid University (KKU) in Almajardah. Out of 95 registered, 87 responded to the questionnaire, and seven students took part in interviews on campus. The findings of the study reveal that recalling Arabic equivalents, boosting confidence, talking about Western culture-related subjects, incorporating particular English terms into their speech, and reflecting long-standing habits are the primary causes that Saudi EFL students switch between English and Arabic. Moreover, EFL students get involved in code-switching to converse about Western society, leveraging their professional backgrounds that inspire them with the image of sophistication and intellect. Regarding patterns of code-switching, the research indicates that Saudi EFL university students occasionally insert English words or phrases while speaking Arabic. They frequently employ both languages in social contexts, particularly for expressions such as “thank you,” “hello,” “sorry,” “goodbye,” and “excuse me.” Furthermore, students regularly replace certain Arabic words with their English counterparts during conversation. In conclusion, the study underscores the need for EFL students to embrace code-switching as a legitimate linguistic strategy. It advocates using contextual language, encouraging emotional expression, prioritizing vocabulary development, integrating media resources, and fostering cultural awareness to enrich the overall learning experience for students.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".