Code Switching as an Interactive Tool in ESL Classrooms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it