Code Switching as an Interactive Tool in ESL Classrooms
Bibliographic record
Abstract
This study addresses the role of code switching to students’ L1 (Arabic) in their ESL classrooms and whether it expands interaction in these classrooms. The gap perceived in this area needs to be addressed towards the domains of sociolinguistics and applied linguistics in the ESL classrooms teaching environment. Henceforth, this study draws on data collected from basic, secondary and college ESL classrooms in the Sudan and Saudi Arabia. The study incorporates various data gathering procedures: audio-taped spoken data of some ESL classrooms, questionnaire and semi-structured interviews. The data has been analysed by descriptive statistics. The findings generally indicate that CS has been used extensively, purposefully and functionally as part and parcel of ESL classrooms’ discourse. The overall findings suggest that, although the use of L1 has been criticized in the existing literature, yet it has been admitted by ESL teachers, showing that L1 use is unavoidable at basic, secondary and tertiary level in the Sudan and Saudi Arabia. In classrooms where both students and teachers share the same L1, there is a great tendency for using it in the fields of explaining meaning and difficult words, guiding interpretation, transmitting lesson content, illustrating grammatical rules, organizing ESL classrooms and praising and encouraging students. Thus, L1 has been found useful in expanding the interactions of ESL classrooms towards facilitating ESL learning process. The study also calls for sensitizing both teachers and students about the helpful uses of CS. Therefore, syllabi and methods of teaching ESL should incorporate CS in an occasional and judicious way.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".