EFL Teachers' and Learners' Perceptions of Code-switching: The Role of Learners' L2 Proficiency Levels
Bibliographic record
Abstract
Code-switching (CS) is a complex linguistic phenomenon in bilingual environments, such as English as a second language (ESL) and English as a foreign language (EFL). In recent years, CS has been viewed as a meaningful linguistic phenomenon in ESL and EFL contexts. This research investigates EFL teachers' and learners' perceptions of the use of CS in Saudi universities. Also, this research aims to investigate the relationship between learners' perceptions of CS and their L2 proficiency levels. A quantitative approach is utilised in this research to collect data from 40 Saudi EFL teachers and 50 Saudi undergraduate EFL learners to investigate their perceptions of CS used in their EFL classrooms. Further, the researcher uses two modified Likert-type questionnaires adopted from Alkhudair (2019) to elicit teachers' and learners' perceptions of CS used in their EFL classrooms. In addition, the Statistical Package for the Social Sciences (SPSS) was used to determine the frequencies, percentages, and mean scores. The learners took Oxford Online Placement Tests to investigate the role of L1 in EFL classrooms. Also, SPSS was used to calculate the Pearson Correlation Coefficient to investigate a correlation between learners' L2 proficiency levels and their perceptions of using L1 in EFL classrooms. As a result, both teachers and learners showed positive attitudes towards CS in EFL classrooms. Moreover, upon investigating the relationship between learners' English language proficiency level and their perceptions of L1 use, the results suggest positive and negative correlations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".