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Record W4399292997 · doi:10.5430/wjel.v14n5p322

Using Smartphones for English Speaking Skill Development from the Omani EFL Learners' Perspectives

2024· article· en· W4399292997 on OpenAlexvenueno aff
Badri Abdulhakim Mudhsh, Yasir Al-Yafaei, Muna Hussain Muqaibal, Mohammed Al-Raimi

Bibliographic record

VenueWorld Journal of English Language · 2024
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceMathematics educationDevelopment (topology)Natural language processingLinguisticsArtificial intelligencePsychologyMathematicsPhilosophy

Abstract

fetched live from OpenAlex

The widespread use of hand-held devices, including smartphones, has been invested in language learning education around the world. This study intends to inspect Omani EFL learners' perspectives on using smartphones to develop their speaking skills. To answer the research questions, the researchers used a 5-point Likert questionnaire to collect data from 259 Omani EFL students from the English Language Unit, Preparatory Studies Center, University of Technology and Applied Sciences (UTAS) in Salalah, Sultanate of Oman. Findings showed positive beliefs about using smartphones. The participants stated that they sometimes use smartphones for oral skills. In the study, a significant difference between female and male students in terms of perceptions towards smartphones was evident, in addition to a positive correlation between the students' levels of study, perceptions, and uses of smartphones. In light of the results, the study provides invaluable recommendations and pedagogical implications for students, teachers, and syllabi designers.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.283
Teacher spread0.267 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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