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Record W1739354588

Speaking Fluency: Technology in EFL Context or Social Interaction in ESL Context?

2011· article· en· W1739354588 on OpenAlexvenueno aff
Taher Bahranı

Bibliographic record

VenueStudies in literature and language · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsFluencyContext (archaeology)PsychologyTest (biology)Social environmentContext effectSocial mediaLinguisticsMathematics educationComputer scienceSociologyWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

Language learning can occur outside the classroom setting unconsciously through interaction with the native speakers or exposure to authentic language input through technology. EFL context lacks the social interaction to boost language learning. Accordingly, this study aimed at investigating the effectiveness of exposure to audio/visual mass media as a source of language input in EFL context and social interaction as a source of language input in ESL context on speaking fluency. To achieve this purpose, a sample speaking test was administered to one hundred language learners in Iran which is an EFL context and one hundred language learners in Malaysia which is an ESL context. Then, forty participants from each context where selected. During the experiment, EFL participants had exposure to audio/visual mass media while the ESL participants had exposure to social interaction. At the end, both groups took another sample speaking test. The post-test showed that the EFL group performed better which proved that exposure to technology promotes speaking fluency. Key words: Exposure; Mass media; Social Context; EFL Context; ESL Context

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.063
GPT teacher head0.329
Teacher spread0.266 · 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 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

Citations22
Published2011
Admission routes1
Has abstractyes

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