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Record W4414707633 · doi:10.17507/tpls.1510.01

Revisiting Academic Reading Teaching: Insights From In-Service EAP Teachers in Bangladesh

2025· article· en· W4414707633 on OpenAlexaff
Md. Zahangir Alam, Iqbal-e Rasul, Farhana Binte Mizan, Zaheed Alam Munna, Mohammad Abu Nayeem

Bibliographic record

VenueTheory and Practice in Language Studies · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsRed River College
FundersSoutheast University
KeywordsReading (process)DisconnectionFormative assessmentEnglish for academic purposesBridge (graph theory)Task (project management)Subject (documents)

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.345
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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