Code Switching and Code Mixing in Teaching and Learning of English as a Second Language: Building on Knowledge
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
The primary goal of language teaching is to afford learners, proficiency in communicating in the target language, self-development as well as intercultural understanding of languages in the learning process. The teacher is therefore charged with the task of selecting appropriate strategies to effectively achieve his pedagogic goals, one of which is the use of Code switching and Code mixing. Traditionally, this strategy has been viewed negatively as signs of deficiencies in a speaker, though in a typical multilingual setting, speakers tend to select multiple codes or mix languages they consider appropriate to facilitate and clarify meanings in their language expressions. This study intends to project the socio- linguistic functions inherent in code switching and mixing that can help ESL students transcend from the known (L1) to the unknown (L2), especially in learning complex language contents; making the teacher’s work, productive and less strenuous. A quantitative methodology was adopted to ascertain the efficacy of code switching and mixing as a teaching strategy. The results revealed that Code switching and mixing have progressive and positive effects in language learning, both for the teacher and learners in the ESL situation.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| 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 it