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Record W2734328663 · doi:10.5539/ijel.v7n4p75

Difficulties Encountered by EFL Students in Learning Pronunciation: A Case Study of Sudanese Higher Secondary Schools

2017· article· en· W2734328663 on OpenAlexvenueno aff
Zahir Adam Daff-Alla Ahmed

Bibliographic record

VenueInternational Journal of English Linguistics · 2017
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
Fundersnot available
KeywordsPronunciationMathematics educationIntelligibility (philosophy)LocalityPsychologyTest (biology)Descriptive researchLinguisticsSociology

Abstract

fetched live from OpenAlex

This study aims at investigating the problems which have been encountered by higher secondary schools students when they try to learn English pronunciation. The problem of this study is that most of the higher secondary school students in Sudan produced incorrect pronunciation for many of English words. So this study is attempted to deal with problem to find the most suitable solutions for it. The participants are Sudanese students of higher secondary schools especially at Shikan locality in Northern Kordofan State. The researcher uses the descriptive analytical approach because it is suitable for such studies. The data of this study is collected by means of recording test and questionnaire. The findings showed that the problems of pronunciation are the result of many teaching difficulties, and the strategies of teaching pronunciation are helpful in producing correct speech pronunciation. Finally the researcher recommends that: Sudanese EFL learners, who are specialized in ELT, should obtain a high level of intelligibility, and the language laboratory should exist in all the higher secondary schools to practice phonetic exercises.

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)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.018
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 science0.0010.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.394
Teacher spread0.361 · 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.

Study designObservational
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

Citations23
Published2017
Admission routes1
Has abstractyes

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