A Health-Check of Communicative Language Teaching (CLT) in Rural Primary Schools of Bangladesh
Bibliographic record
Abstract
Bangladesh ELT situation has been deteriorating for the last four decades. Regional and national projects including ELTIP and EIA proved to be futile in improving this situation, especially, in the Bangladesh rural primary schools with almost 0% properly trained English teachers to implement the current CLT curriculum. This article investigates the on-going poor health-status of CLT at randomly selected schools of northern Bangladesh through delving into the research gaps linked with the ELT practitioners’ own English proficiency, their training needs, motivation and teaching skills; their perception on the instructional module; and perception of the YLs of English in Bangladesh. This enquiry uses a mixed method involving a questionnaire survey, semi-structured interview with ELT teachers, teacher trainers, head-teachers and lesson observation followed by a workshop and informal discussion with 100 participants from all ELT stake-holders to validate the findings of the earlier questionnaire survey. The findings conclude that ineffective and lack of teacher training, non-availability of English subject teacher, unproductive instructional materials, unhelpful learning environment, learners’ socio-economic background, teachers’ poor competence in English and knowledge of CLT methods are responsible for this deterioration. It recommends that implementation of a rigorous teacher training program for CLT to produce English subject teachers for each school, production of a pedagogically user-friendly instructional module for CPD, a culture-oriented teaching-learning environment and a program of regular guidance and supervision by CLT experts would address the gaps prevailing in the ELT (and CLT) situation at the rural primary schools of Bangladesh.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".