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Record W3007268074 · doi:10.5539/elt.v13n3p100

The Fluency Way: A Functional Method for Oral Communication

2020· article· en· W3007268074 on OpenAlexvenueno aff
Faramarz Samifanni

Bibliographic record

VenueEnglish Language Teaching · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyFluencyLanguage acquisitionSecond-language acquisitionCompetence (human resources)CognitionQualitative researchMathematics educationPedagogyLinguisticsSocial psychology

Abstract

fetched live from OpenAlex

This study delves into the analysis of theories of language acquisition and teaching methods. A qualitative approach was used to analyze three language acquisition theories and five teaching methods to help and provide teachers with methods that are relevant and applicable in developing and enhancing the speaking skills of the students. Findings revealed that grammatical skill is not the focus of Second Language Acquisition. It is concluded that language learning and acquisition are dynamic activities requiring social, cultural, and cognitive competencies on the part of the teacher. The teaching of language is most effective and productive when the learners are actively engaged in authentic tasks that are conducted in a natural and spontaneous manner to enable learners to gain a total psycho-socio-cultural-linguistic experience. Learner-centeredness is a universal theme and the teacher-learner relationship is a cooperative and collaborative partnership for relevant and functional language competence. Suggestions and recommendations for future studies were proposed.

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)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.020
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.002
Science and technology studies0.0020.003
Scholarly communication0.0040.005
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.004

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 head0.040
GPT teacher head0.291
Teacher spread0.251 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations7
Published2020
Admission routes1
Has abstractyes

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