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Record W4388495456 · doi:10.5430/wjel.v14n1p111

Attitudes and Opinions of Kuwaiti EFL Instructors about Online Distance Learning

2023· article· en· W4388495456 on OpenAlexvenueno aff
Seham Al-Abdullah, Mohammad Almutairi

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationThematic analysisContext (archaeology)Online learningComputer scienceCoronavirus disease 2019 (COVID-19)English as a foreign languagePsychologyQualitative propertyMathematics educationData collectionMedical educationQualitative researchMultimediaSociologyMedicine

Abstract

fetched live from OpenAlex

This study investigates the attitudes and opinions of Kuwaiti instructors of English as a Foreign Language (EFL) on the shift from face-to-face classes to online distance learning during the COVID-19 pandemic. A mixed-methods approach to data collection and analysis was utilised. A total of 70 EFL instructors participated in this study. Quantitative and qualitative data were collected using a questionnaire that contained closed- and open-ended items. Quantitative data were descriptively analysed using Microsoft Excel Software, and thematic analysis was used for the responses to the open-ended questions. Results revealed participants’ satisfaction with their online teaching experience, demonstrating positive attitudes towards online distance learning. Findings also revealed that several advantages characterised the shift to online distance learning. The unexpected transition also resulted in some drawbacks and difficulties. The study has pedagogical and practical implications for educational decision-makers and EFL instructors that must be considered. The findings contribute to the literature on EFL teachers’ attitudes and opinions on online distance learning by providing data from the Kuwaiti educational context.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.308
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.343
Teacher spread0.325 · 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
Published2023
Admission routes1
Has abstractyes

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