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Record W1969438174 · doi:10.5539/elt.v5n6p78

Exploring When and Why to Use Arabic in the Saudi Arabian EFL Classroom: Viewing L1 Use as Eclectic Technique

2012· article· en· W1969438174 on OpenAlexvenueno aff
Asim Mohammad Khresheh

Bibliographic record

VenueEnglish Language Teaching · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologySemitic languagesCommitArabicClass (philosophy)Literal translationMathematics educationLinguisticsNorm (philosophy)Computer scienceSource textArtificial intelligence

Abstract

fetched live from OpenAlex

This study aims to investigate when and why to use Arabic as L1 in the Saudi Arabian EFL classroom. For this purpose, 45 classroom observations were performed for beginning, intermediate, and advanced levels of students. 5 classes were chosen randomly for each level and each class was observed three times. Based on the classroom observations, structured interviews were conducted with 94 students as well as 15 teachers. Analysis of the data shows that Arabic can be used as eclectic technique in certain instances regardless of what teaching method is employed. For example, teachers sometimes used it as long as they talk in English for a long time so as to avoid as grammatical mistakes as possible. This reflects the teachers’ cultural norm, namely, it is shameful to commit mistakes in front of the students. In addition, it is apparent that learners follow certain language strategies such as literal translation and substitution. Despite the use of these strategies, L2 speech produced by some learners is sometimes difficult to understand because of their bad command in English particularly at the beginning and intermediate level. Thus, the teachers or the learners resort to use Arabic forms or translation as a way to explain what wanted to be conveyed in English. Besides, Arabic is used when students are not able to express difficult L2 constructions at time they could not be disallowed to use Arabic counterparts as they are dynamic individuals. On the contrary, some advanced students insist to use specific Arabic concepts although they can translate them into English because, as they believed, such concepts miss their cultural and religious value if translated.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0000.000
Research integrity0.0000.002
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 head0.107
GPT teacher head0.275
Teacher spread0.168 · 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.

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

Citations32
Published2012
Admission routes1
Has abstractyes

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