Revisiting Academic Reading Teaching: Insights From In-Service EAP Teachers in Bangladesh
Bibliographic record
Abstract
The goal of this study was to see how teachers' beliefs about teaching academic reading skills in English for Academic Purposes (EAP) courses aligned with their actual teaching practices at a Bangladeshi university. Academic reading is a vital skill for students pursuing higher education, yet discrepancies often exist between teachers’ theoretical understanding of the subject and classroom execution. Using a qualitative approach, the study employs multiple case studies of three EAP instructors, incorporating semi-structured interviews and classroom observations. The findings revealed a significant misalignment between teachers’ beliefs and practices. Although teachers support student-centered approaches such as scaffolding and peer interaction, their classroom practices remain largely teacher-centered, with an overemphasis on teacher talk, controlled textbook tasks, and limited formative assessments. This disconnection between beliefs and practices limits opportunities for active learning and student engagement. This study highlights gaps in aligning lesson objectives, task design, and scaffolding with students’ needs. These findings suggest the need for more reflective, student-centered pedagogy to bridge the gap between teachers’ intentions and classroom realities, ultimately enhancing academic reading instruction for EAP students.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".