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Record W4284895303 · doi:10.5430/wjel.v12n6p29

Online Collaborative Flipped Writing Classroom: A Framework for Online English Writing Instruction

2022· article· en· W4284895303 on OpenAlexvenueno aff
Syarifudin Syarifudin, Husnawadi Husnawadi

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsIndonesianContext (archaeology)Flipped classroomComputer scienceCoronavirus disease 2019 (COVID-19)English languageCollaborative learningPandemicMathematics educationPedagogySociologyPsychologyKnowledge managementLinguistics

Abstract

fetched live from OpenAlex

The Covid-19 pandemic has brought about unprecedented global impacts, particularly in the ELT sector. Empirical evidence has shown that English language teachers and students face several challenges, namely the inadequacy of technological skills, learning hours, motivation, and engagement, to mention a few. However, a few studies offer an applicable learning framework that provides theoretical and practical insights for L2 or English writing instructors, particularly in the Higher Education (HE) context amid the pandemic. Therefore, this paper proposes an applicable learning framework, “Online Collaborative Flipped Writing Classroom” (OCFWC), for L2 writing instruction in HE as a remedy for remote learning conditions and beyond. The initial section of this paper sheds light on the rationale for establishing the learning framework, followed by a brief overview of the theoretical underpinnings of this framework. Second, it presents the step-by-step procedures for implementing this learning framework by drawing on an English language writing classroom in an Indonesian HE context during the Covid-19 pandemic. The final section of this paper discusses the pedagogical benefits, limitations, and implications for future studies.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.343
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.269
Teacher spread0.252 · 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
Published2022
Admission routes1
Has abstractyes

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