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Record W3216279850 · doi:10.5539/elt.v14n12p171

The Impact of EFL Teachers’ Pedagogical Beliefs and Practices: Communicative Language Teaching in A Saudi University Context

2021· article· en· W3216279850 on OpenAlexvenueno aff
Ahmed Alghamdi

Bibliographic record

VenueEnglish Language Teaching · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyCommunicative language teachingContext (archaeology)English as a foreign languageMathematics educationGrammarTeaching methodForeign languageImplementationPedagogyLanguage educationLinguisticsComputer science

Abstract

fetched live from OpenAlex

This study aims to explore the pedagogical beliefs of Saudi instructors of English as a foreign language (EFL), and the extent to which they apply the values of the communicative language teaching (CLT) approach in their classroom practice. The study was conducted with 42 Saudi EFL teachers and employed a mixed methods approach. A descriptive analysis of classroom observation data was conducted. The results showed that teachers hold positive views of CLT, but that there are some discrepancies between their beliefs and their implementations of the approach. For example, most of the instructors continued to apply traditional teaching methods (i.e., grammar translation and the audio-lingual approach). The study concludes that it is essential in the Saudi EFL context for teachers to cultivate relations between their beliefs and practices to assure better language learning outcomes. The key contribution of this study lies in disclosing the reasons for the discrepancies between Saudi EFL teachers’ beliefs and practices to help them develop congruence, and in highlighting the pedagogical implementations.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.347
Teacher spread0.292 · 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 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

Citations12
Published2021
Admission routes1
Has abstractyes

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