Patterns of Interaction on Peer Feedback: Pair Dynamics in Developing Students’ Writing Skills
Bibliographic record
Abstract
The study investigated students’ patterns of interaction and their viewpoints toward incorporating peer feedback in L2 writing class, making use of a video stimulated recall (VSR) interview and the compositions. Data were analyzed qualitatively; two groups of six students with mixed English proficiency were analyzed in terms of the language-related episodes (LREs). The participants of the study were 21 undergraduate students, majoring in English in a university in the three southernmost border provinces of Thailand. For data analysis, peer dialogues were recoded, transcribed and coded to identify students’ patterns of interactions in terms of collaborative, expert/novice, dominant/dominant and dominant/passive patterns, based on Storch’s (2002) scheme. Moreover, the findings revealed that the students’ English proficiency level did not influence the LREs and their writing ability. Additionally, students’ writing performance was improved in the identified patterns of the collaborative and expert/novice instances. Specifically, students perceived the writing process, developed affective strategies, reinforced their critical thinking ability and enhanced their social interaction skills. Besides, it encouraged them to become more effectively autonomous learners. Hence, peer feedback should be implemented in L2 writing.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 0.000 |
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.
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".