Code Switching: Awareness Amongst Teachers and Students in Saudi Universities EFL Classrooms
Bibliographic record
Abstract
This study is a comprehensive investigation of Saudi university EFL classroom interactants’ [faculty & students] awareness towards various dynamics of code switching (hereinafter CS). The participants comprised of 100 faculty members and 100 students drawn from Taif University English Language Center [henceforth TUELC]. A 22 item questionnaire was adapted on a Likert-scale to elicit their perceptions related to various functions of CS in a Saudi EFL classroom context. The results revealed that both groups [faculty & students] indicated almost the same results as far the attitudes towards the reasons that prompt CS in the EFL classroom are concerned but they showed comparatively wider differences towards perceptions of the awareness of CS in Saudi EFL universities classrooms (section 1) and perceptions of the subjective norms of CS in Saudi EFL universities classrooms (section 2) of the questionnaire. The findings of the study reveal that Saudi EFL classroom interactants bear quite positive attitudes towards CS. It is also found that the participants of this study have a strong urge to learn the English language and for the specified purposes of the role of CS is authenticated by the respondents. During the analysis, the results of the study indicated that both respondents [i.e. faculty & students] agreed to use CS, but utility of different functions vary in their perceptions. Moreover, in the light of the data analysis, trends were determined among groups to measure the significance of each function of CS. In short, this work tried to understand the significance of mother tongue [hereinafter MT] and target language [henceforth TL] in the EFL context of Saudi universities.
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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 | 0.005 |
| 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".