Online Collaborative Flipped Writing Classroom: A Framework for Online English Writing Instruction
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".