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Record W1542195164

The Effect of Vowel-Recognition Training on Beginner and Advanced Iranian ESL Learners

2013· article· en· W1542195164 on OpenAlexaboutno aff
Khaghaninezhad

Bibliographic record

VenueJournal of teaching language skills · 2013
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
Fundersnot available
KeywordsVowelTest (biology)PsychologyPhonologyLinguisticsComputer scienceSpeech recognition
DOInot available

Abstract

fetched live from OpenAlex

This study was an attempt to investigate the effect of vowelrecognition training on beginner and advanced Iranian ESL learners. A total of 36 adult Iranian ESL learners (18 advanced and 18 beginners) who were students of various majors at Memorial University (MUN) were recruited for the study. Advanced participants had the experience of living in Canada for at least three years while beginners had lived in Canada less than six months. The study commenced with a pre-test to verify the participants’ awareness of English vowel sounds. Predictably, advanced participants were superior to beginners in terms of English vowel awareness. After the pretest administration, participants of both groups underwent a five-week vowel-recognition training course (focusing on all English vowel sounds). At the end of the vowel-recognition training program, the 80-item multiple choice test which had been once used as the pre-test was conducted again. The findings revealed that both beginner and advanced participants’ performance was improved on the second administration of vowel-identification test due to the intensive vowel-recognition training program. It is also revealed that formal instruction of English vowels had raised the beginners’ English phonetic knowledge to that of advanced learners. Moreover, it was shown that phonology of the participants’ first language (i.e. Farsi) did have an impact on the acquisition of second language phonological features.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.900
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.011
GPT teacher head0.327
Teacher spread0.316 · 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 designOther design
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

Citations1
Published2013
Admission routes1
Has abstractyes

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