Attention to Form in Collaborative Writing Tasks: Comparing Pair and Small Group Interaction
Bibliographic record
Abstract
Abstract: This study examines the opportunities that a collaborative writing task completed in pairs and in small groups offers for attention to form. Previous research suggests that collaborative writing activities encourage learners to focus their attention on language and to collaborate in the resolution of their language-related problems in ways that facilitate learning. While that research focused almost exclusively on dyads, the present study compares the performance of the same writing task by learners working in pairs (n = 64) and in groups of four (n = 80). It investigates the role played by the number of participants on the frequency, resolution, and length of language-related episodes (LREs) focused on Spanish past tense morphology. It also examines the learners’ level of engagement in these LREs. Findings indicate that both groups and pairs focused their attention on form relatively often, but groups produced a significantly higher number of past tense LREs and were also more successful at solving them. As a result, their texts were more accurate. The LREs produced by the groups were also longer and showed more evidence of elaborate engagement with past tense morphology, therefore providing enhanced opportunities for second language learning. The pedagogical implications of these findings are discussed.
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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 | 0.005 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".