Professors’ and Undergraduate Students’ Perceptions and Attitudes Toward the Use of Code-Switching and Its Function in Academic Classrooms
Bibliographic record
Abstract
This paper aims to determine the usefulness and functions of code-switching in the classroom when used by both lecturers and students. The study was conducted at a Saudi university campus and follows a quantitative approach using sets of questionnaires to collect the data. Then, the data were analyzed using SPSS (Statistical Package for the Social Sciences). Based on the analysis, the given study shows that professors and students consider code-switching from English into Arabic in the classroom as a beneficial instrument in enhancing the learning process. That is because it allows otherwise insurmountable problems for two-way communication in the L2 target language (English) to be overcome. Also, the findings of the current study reveal a range of positive attitudes toward using code-switching in two ways, Saudi-English immersion classrooms. Specifically, the majority of the professors use this technique in certain situations to achieve a better understanding by the students. In the same vein, students in this study show huge acceptance and prove how such a phenomenon has worked as a learning facilitator. It is hoped that the results of this study will be useful for professors and researchers investigating the importance of code-switching in the classroom.
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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 | 0.011 |
| 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) | 0.002 | 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".