Comparative Study on Instructors’ and Students’ Code-Switching in an EFL Class
Bibliographic record
Abstract
This study investigates code-switching (CS) occurred between instructors and students in an English as a foreign language (EFL) class at an international college in China. Questionnaires and in-class observations were carried out among both instructors and students to elicit the data. Three aspects are investigated: frequency of CS, reasons for CS and attitudes towards CS. It was found that (1) English (TL) stays dominant while Chinese (L1) was auxiliary in an EFL class. (2) Most instructors and students use code-switching in class, which can be attributed to many factors. For students, low English proficiency was the underlying reason, while for instructors, major reason lies in translating important parts. Most of them are positive towards CS. Pedagogical implications of the findings were also discussed. Overall, this study contributes to teaching English as a foreign language (TEFL) based on empirical and experimental results.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".