Challenges of Teaching English Language in a Multilingual Setting: An Investigation at Government Girls Secondary Schools of Quetta, Baluchistan, Pakistan
Bibliographic record
Abstract
Baluchistan is a multi-linguistic and multi-ethnic province of Pakistan. In this region, the inhabitants for communication purpose speak a number of local languages such as Baluchi, Brahvi, Pashto, Saraiki, Punjabi, Urdu, Sindhi and Persian. Students who attend the government schools speak these languages. This study aimed to explore the challenges faced by the secondary school female teachers while teaching the English language in their multilingual classrooms. The purposive sampling was used and 10 government secondary school female teachers participated in the study. The data was collected through a semi-structured interview protocol and classroom observation checklist. The data was analyzed by using thematic analyses technique. The findings of the study revealed a number of challenges. Teaching English language in a multilingual context is an enormous challenge for the English teachers due to linguistic diversity in the classrooms. The students in the multilingual classrooms lack confidence to use English language because they hesitate to commit mistakes. The curriculum may be inappropriate for helping students to improve their English proficiency. In the multilingual classrooms code-switching is commonly used by the teachers to instruct the students. The study suggests that; the teachers may be trained to cope with the challenges they face in their multilingual classrooms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".