Investigating the Functions of Code-Switching among EFL Lecturers and Undergraduate Students in Saudi Arabia
Bibliographic record
Abstract
Many researchers have noticed that instructors who teach English as a foreign language (EFL) in language-learning institutions worldwide frequently implement code-switching (CS). Through this study, we investigate the functions of CS, specifically topic-switching, affective, and repetitive functions, as a pedagogical tool in an English for Specific Purposes classroom. The study is based on the perceptions of lecturers and students from two higher education institutions, Digital Colleges and Yanbu Industrial College, in Saudi Arabia. A quantitative research design was employed, with data collected through a questionnaire. The study incorporated a sample of 24 female EFL lecturers and 193 Saudi female students. The results indicate that both groups acknowledge using CS for topic shifting, with students displaying slightly higher acceptance. Additionally, both lecturers and students recognize the affective aspects of code-switching, with the students exhibiting greater agreement. Furthermore, lecturers and students agree regarding clearly identifying all repetitive functions. The study highlights the potential of CS functions as an educational tool to enhance interaction between EFL lecturers and students, encouraging engagement, cooperation, and academic achievements. It also underscores the significance of determining appropriate functions for various circumstances and shows that although acceptance may differ, there is general agreement on the various purposes for which CS is utilized in the EFL context.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".