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Record W1618712101 · doi:10.1111/modl.12188

Textual Input Enhancement for Vowel Blindness: A Study with Arabic ESL Learners

2015· article· en· W1618712101 on OpenAlexafffund
Reem Alsadoon, Trude Heift

Bibliographic record

VenueModern Language Journal · 2015
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsVowelArabicPsychologyFocus (optics)Decoding methodsLinguisticsBlindnessWord (group theory)AudiologyComputer scienceSpeech recognitionOptometryMedicine

Abstract

fetched live from OpenAlex

This study explores the impact of textual input enhancement on the noticing and intake of English vowels by Arabic L2 learners of English. Arabic L1 speakers are known to experience vowel blindness, commonly defined as a difficulty in the textual decoding and encoding of English vowels due to an insufficient decoding of the word form. Thirty beginner ESL learners participated in a training study during which the experimental group received textual input enhancement on English vowels. Students completed a pretest and an immediate and delayed posttest. An eye‐tracker recorded students' eye fixations during the treatment phase. Results indicate that vowel blindness was significantly reduced for the experimental group who received vowel training in the form of textual input enhancement. This might be due to a longer focus on the target words as suggested by our eye‐tracking data.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.067
GPT teacher head0.389
Teacher spread0.322 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations19
Published2015
Admission routes2
Has abstractyes

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