Evaluating the Efficacy of Code-Switching as a Strategy for Enhancing ESL Instruction
Bibliographic record
Abstract
In this study I aim to evaluate the effectiveness of code-switching (CS) as a strategy to enhance teaching English as a second language and the usefulness of CS in enhancing students’ language acquisition, comprehension, and communication skills. The study sample included 52 English language teachers at Technical and Vocational Training Corporation in different colleges. I use a mixed-methods approach, which is a content analysis method of collecting data on the use of CS, as well as a questionnaire to collect information from English teachers about their experiences with CS in enhancing language acquisition, comprehension, and communication skills. The results showed that teachers believe that CS is a useful strategy in the ESL classroom. Moreover, there was a difference in the levels of CS in the classroom, which could be attributed to differences in teachers’ educational experiences, the level of the students' language acquisition they were teaching, and their teaching styles. The percentage of CS ranged from 13.5% to 25.7%, which indicates different degrees of reliance on CS as a communication tool.
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.
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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".