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Record W2789042788 · doi:10.5539/ijel.v8n3p177

English Language Teaching and Cultural Implications in Saudi Arabia

2018· article· en· W2789042788 on OpenAlexvenueno aff
Muhammad Umar Farooq, Abdul Fatah Soomro, Muhammad Umer

Bibliographic record

VenueInternational Journal of English Linguistics · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionTarget cultureLanguage educationForeign languageFirst languagePsychologyEnglish languageLanguage assessmentPedagogyMathematics educationSociologyLinguistics

Abstract

fetched live from OpenAlex

Culture and language are inseparable. Language is used both as a means of communication as well as a carrier of culture. Language without culture is unthinkable, so is human culture without language (Wei, 2005). While teaching a foreign language, a teacher needs to accustom himself with the culture of the target language and the culture of learners. However, teachers in different classrooms in different parts of the world still ignore the importance of culture as a part of language study. This study focuses on perception and practices of teachers regarding culture and English language teaching in Saudi Arabia. The data were collected from 121 (60 male and 61 female) teachers of different cultural background through a questionnaire on their perceptions and implication of teaching target culture in the classroom through English language courses. It was revealed that all teachers have knowledge about the aspects of the target culture to some extent, but how they deal with the target culture is affected highly by the curricular considerations and limitations.

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)0.001
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 score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.306
Teacher spread0.283 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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