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Record W2103545744 · doi:10.5539/elt.v4n4p121

ESP Needs Analysis of Iranian MA Students: A Case Study of the University of Isfahan

2011· article· en· W2103545744 on OpenAlexvenueno aff
Fatemeh Moslemi, Ahmad Moinzadeh, Azizollah Dabaghi

Bibliographic record

VenueEnglish Language Teaching · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMathematics educationNeeds analysisForeign languageTest (biology)English for specific purposesSubject (documents)Medical educationPedagogyLibrary scienceMedicineComputer science

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the foreign language learning needs of Iranian MA students, in particular those who were majoring in biology, psychology, physical training, accounting and west philosophy. A total of 80 students from five MA majors studying at university of Isfahan participated in the study. Additionally, twenty- five subject-specific instructors as well as seven English instructors took part in the study. The study was designed on qualitative and quantitative survey basis using interviews, questionnaires, and texts. In order to investigate participants’ point of views, chi-square test was used to analyze the data. The result obtained revealed that majority of the participants were dissatisfied with the current ESP courses for MA students. Most of the participants asked for an urgent need for revision and reconsideration of English instruction in the Iranian educational system as well as universities, stating that Iranian students do not have enough exposure to English language in a way that help them to fulfill their subjective and objective needs at MA level . Giving more weight to English in the MA entrance exam was suggested as one possible solution. It was thought that this would increase the motivation of the students to improve their language proficiency; furthermore, joint teaching of the ESP courses was suggested as another solution to help students meet their English needs at the MA level.

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)0.000
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 score0.091
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.025
GPT teacher head0.240
Teacher spread0.215 · 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

Citations31
Published2011
Admission routes1
Has abstractyes

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