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Record W1975973220 · doi:10.5539/ells.v1n2p14

English for B. Sc. Students of Physical Education in Iran: A Study of Perception of English Needs and Effectiveness of ESP Textbooks

2011· article· en· W1975973220 on OpenAlexvenueno aff
Mohammad Reza Hashemi, Amir Rashid Lamir, Farideh Rezaee Namjoo

Bibliographic record

VenueEnglish Language and Literature Studies · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsNeeds analysisPerceptionCurriculumEnglish for specific purposesPsychologyMedical educationPerspective (graphical)Mathematics educationEnglish languageEnglish for academic purposesPedagogyMedicineComputer science

Abstract

fetched live from OpenAlex

EAP/ESP plays an important role in countries where English is used mainly for academic purposes. However, EAP/ESP programs have been developed without conducting a systematic needs analysis from both the students’ and instructors’ perspective. The purpose of the present study is to shed more light on the perception that Iranian undergraduate students and the faculty of Physical Education have of the English language needs of the students and the shortcomings of the commonly used textbook in EAP/ESP courses at universities. A total number of 112 students of P.E (47 male and 65 female) participated in the needs analysis procedure of the present study. They ranged from 21 to 27 years of age and were all undergraduates studying at the Physical Education Faculty, Ferdowsi University of Mashhad. Four P.E. faculty members, all holding Ph.D. were also interviewed. Results of the present study indicate that English is perceived as important by Iranian P. E. students and the faculty, and show discrepancy between the perceptions of the learners and instructors. The study has implications for curriculum design and instructional delivery of ESP/EAP courses for undergraduate P.E. 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)0.001
metaresearch head score (Gemma)0.001
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 score0.014
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.285
Teacher spread0.269 · 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

Citations7
Published2011
Admission routes1
Has abstractyes

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