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Record W2888132282 · doi:10.5539/elt.v11n9p144

Explicit Versus Implicit Modes of EFL Reading Literacy Instruction: Using Phonological Awareness With Adult Arab Learners

2018· article· en· W2888132282 on OpenAlexvenueno aff
M Mansur Ibrahim

Bibliographic record

VenueEnglish Language Teaching · 2018
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsPhonological awarenessPsychologyContext (archaeology)LiteracyReading (process)Mathematics educationSample (material)PedagogyLinguistics

Abstract

fetched live from OpenAlex

There is a strong belief among language teachers that intensive exposure to language alone can lead to acquisition. The present study is a qualitative- quantitative research design that used a quasi-experimental design to compare between the effectiveness of intensive exposure to language versus phonological awareness explicit instruction on adult Arab EFL learners’ EFL reading literacy in a Saudi university context. The study is also concerned with Saudi adult learners’ attitudes towards phonological awareness instruction. Participants (N=89) were all male students enrolled in an intensive EFL undergraduate program, where they were required to pass an intensive EFL course. Sample were randomly divided into a treatment group (N=47), who received phonological awareness treatment, and a control group (N=42) who were exposed to language intensively. Posttest findings confirmed the significant effect of phonological awareness instruction on the sample’s EFL literacy. Moreover, positive attitudes towards the program were detected in the interviews held at the end of treatment. Therefore, the study recommended introducing phonological awareness instruction to develop Arab learners’ EFL literacy.

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)0.003
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.335
Teacher spread0.312 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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