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Record W4318200549 · doi:10.5430/wjel.v13n2p111

Conflicting Student Viewpoints about Online EFL Learning during the Covid-19 Pandemic: Focus on Effective Learning and Well-being

2023· article· en· W4318200549 on OpenAlexvenueno aff
Laura Naka

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsViewpointsCoronavirus disease 2019 (COVID-19)PandemicFocus groupOnline learningPsychologySocial mediaComputer scienceFocus (optics)English as a foreign languageMedical educationMathematics educationQualitative researchMultimediaSociologyWorld Wide WebMedicine

Abstract

fetched live from OpenAlex

Due to the total shutdown caused by the COVID-19 pandemic, worldwide countries regardless of their development shifted instruction to the online learning method. This study aims to investigate the views of students in undergraduate studies about the impact of online (EFL) English foreign language learning on educational issues and well-being. Through questionnaires and focus group discussions, participants revealed their real sensitivities and views about online EFL learning, thus both quantitative and qualitative research methods are implemented in the present study. Their experiences showed the advantages that supported their needs during a certain period such as the pandemic, but also the challenges they went through during that time. The study presents social and educational implications that are directly related to technology and environmental conditions during online learning that affected the well-being of students. Results showed that shifting to online classes uncovered contradictory preferences among students regarding online learning and teaching. Finally, this study provides an overview, that in case the implementation of online or hybrid learning is requested again in the future, the necessary preparations that meet the educational and social needs of the students should be taken into account.

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)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.383
Teacher spread0.367 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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