Current Practices and Pitfalls of ELT Syllabi for Developing Engineering Students' Communicative English in Bangladesh*
Bibliographic record
Abstract
The current practices and shortcomings of engineering students' English language teaching (ELT) syllabi were examined from the perspectives of learners and teachers in English as a foreign language (EFL) context. The syllabi included content that had little impact on students' communicative competence in English (CC-E). Students were generally concerned about their professional communication abilities. In this study, the researchers collected data from ten engineering-focused universities in Bangladesh. These universities' existing ELT syllabi (ELT-S) were examined, seeking the current practices and pitfalls. The study sampled 152 participants from the selected universities. The study followed a mixed-method approach. In the qualitative technique, content analysis, focus group discussion (FGD), and interviews were employed for data collection, while survey questions were used in the quantitative approach. The study's findings revealed that the existing English syllabi of those selected universities required updating and modification to meet the identified professional requirements regarding the type, credit allotment, content, classroom practices, class size, policies, etc. The improvements included redesigning English syllabi, material, and teaching methods to improve engineering students' communicative abilities. A uniform curriculum with at least one English language sessional course per semester in all engineering majors was strongly recommended.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".