7. Thai EFL learners’ interaction during collaborative writing tasks and its relationship to text quality
Bibliographic record
Abstract
Second language (L2) writing research has shown that L2 learners routinely scaffold each other when working together to co-construct written texts. The analysis of peer interaction has focused largely on the occurrence of language-related episodes (LREs), with fewer studies documenting how learners discuss other elements of written texts, such as their content or organization (Elola & Oskoz 2010; Storch 2005; Storch & Wigglesworth 2007; Wigglesworth & Storch 2009), or establishing a link between student interaction and text quality. This chapter describes the interaction that occurred when Thai EFL students worked in pairs to write summary and problem/solution paragraphs and explores whether their discussions were related to text quality in the form of analytic ratings. The results indicated that problem/solution collaborative writing tasks showed a positive relationship between student talk and text quality and elicited significantly more discussion of content, organization, and language than summary tasks. Implications are discussed in terms of pedagogical considerations for the use of collaborative writing tasks in EFL contexts.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".