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Record W3052796949 · doi:10.5539/elt.v13n9p18

Code-switching Versus Target-language-only for English as a Foreign Language: Saudi Students’ Perceptions

2020· article· en· W3052796949 on OpenAlexvenueno aff
Mazeegha Ahmed Al Tale’, Faten A. Alqahtani

Bibliographic record

VenueEnglish Language Teaching · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsSustenancePsychologyReading comprehensionReading (process)PerceptionComprehensionMathematics educationEnglish as a foreign languageForeign languageSignificant differencePedagogyLinguistics

Abstract

fetched live from OpenAlex

Selecting the medium of instruction to teach English as a Foreign Language (EFL) has been a controversial issue for several years. This article explores the impact of code-switching (CS) versus target-language-only (TL-only) teaching strategies on the learning and affective sustenance of EFL reading comprehension beginner students based on their perceptions. It also investigates whether there is a significant difference between the participants’ perceptions of these two teaching strategies’ possible impact on their learning and affective sustenance. Fifty-two female Saudi college students participated in the study. A questionnaire and follow-up interviews were used to collect the data. The results indicate that the participants had positive perceptions about the impact of CS on their learning and affective sustenance in the EFL reading classes as opposed to negative perceptions about TL-only instruction. The results also show that there is a significant difference between their perceptions of TL-only instruction and CS, indicating that they prefer CS to TL-only instruction in their EFL reading classes. We recommend that reading comprehension teachers for beginners utilize CS as a facilitating instructional strategy for EFL beginners to give them affective support and make the input more comprehensible.

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)0.001
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.022
GPT teacher head0.285
Teacher spread0.264 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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