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Record W2229794902 · doi:10.5539/elt.v9n2p20

EFL Teachers’ Cognition of Teaching English Pronunciation Techniques: A Mixed-Method Approach

2016· article· en· W2229794902 on OpenAlexvenueno aff
Melor Md Yunus, Hadi Salehi, Mahdi Amini

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPronunciationPsychologyCognitionMathematics educationMetacognitionEnglish languageQualitative researchTeaching methodLinguistics

Abstract

fetched live from OpenAlex

<p>In recent years, a great number of attempts have been made on teachers’ cognition with the aim of understanding the complications reinforcing the teachers’ cognitions and their classroom practices. Such studies shed light on how teachers’ cognitions expand over time and how they are reflected in their classroom practices. The aim of the present study was to investigate Iranian EFL teachers’ cognition particularly in terms of the pronunciation techniques they apply in the oral communication classrooms and their knowledge about their language learners’ characteristics. To achieve the goals of the study, the cognitions of five English teachers in the oral communication classrooms were explored. The teachers were requested to answer two semi-structured interviews to obtain the data about their cognitions regarding the pronunciation techniques. Furthermore, their students were asked to fill out a questionnaire to express their opinions about the techniques applied by their teachers during instruction of English pronunciation. The qualitative and quantitative results showed that there was an intricate relationship between language teachers’ experience with their cognitions about their language learners. Moreover, those teachers who were in higher level language courses showed to have broader cognitions about both the techniques they used in classrooms and the language learners’ characteristics as well.</p>

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)0.005
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.014
GPT teacher head0.261
Teacher spread0.247 · 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 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

Citations21
Published2016
Admission routes1
Has abstractyes

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