A Case Study of ASEAN EFL Learners’ Collaborative Writing and Small Group Interaction Patterns in Google Docs
Bibliographic record
Abstract
The study investigated the interaction patterns of six ASEAN EFL university students when they worked in small groups on two collaborative writing tasks: a descriptive essay and an argumentative essay. Both groups were homogeneous in terms of gender and heterogeneous in terms of home countries. Data collection included pre- and posttest writing, pre- and post-task questionnaires, participants’ work on essays, their reflections, observations, and semi-structured interviews. The students worked on their essays in Google Docs, and the researcher(s) used DocuViz as a tool for visualizations of students’ collaborative writing contributions and styles. The findings showed different interaction patterns (a cooperative revision style for Group A vs. a main writer style for Group B) across the two collaborative writing tasks. While revising, both groups added and corrected their essays and employed almost the same writing change functions and language functions, which were suggesting, agreeing, and stating.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".