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Record W4321768733 · doi:10.5430/wjel.v13n3p104

WhatsApp as a Supporter Tool in Language Learning: A Study of Saudi EFL Learners’ Perceptions

2023· article· en· W4321768733 on OpenAlexvenueno aff
Mohammad Yahya Ali Bani Salameh

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSupporterPerceptionAutonomyMathematics educationPsychologyEnglish languageLanguage acquisitionLearner autonomyComputer sciencePedagogyLanguage educationComprehension approachPolitical science

Abstract

fetched live from OpenAlex

Technology has literally become inseparable from modern day living as it always stays with us in all places and times. Therefore, prudence demands that teachers too, make positive use of it for language learning/teaching. This study compares the impact of WhatsApp based learning activities (WBLA) with conventional activities on boosting the four language skills of Saudi EFL students. The study also investigates Saudi EFL students' perceptions on using WBLA as a tool of English language learning, motivation and autonomy. A close-ended questionnaire was developed to collect data from 100 Saudi EFL undergraduate students at Duba, University of Tabuk, the subjects of this study. The investigation showed that WBLA has a positive impact on shaping the Saudi EFL undergraduates’ perceptions to language learning. Moreover, results indicate that male learners outperformed females in all the four skills. The study, therefore, concludes that WBLA should be integrated in all EFL classes. This study establishes that novel use of technology offers effective assistance in the language classroom.

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)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.008
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.343
Teacher spread0.326 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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