Raising language awareness in peer interaction: a cross-context, cross-methodology examination
Bibliographic record
Abstract
In this paper, both a cognitive and a sociocultural theoretical perspective are used to bring together findings from two studies that investigated the effects of instruction designed to enhance the potentially positive effect of peer interaction on L2 development. Despite differences between the studies’ learning contexts, participants’ age, and research design, a comparative examination of their findings promotes our overall understanding of whether and how instruction that teaches students to engage in reciprocal L2 learning can be effective. In Study 1, Japanese university students were taught how to provide corrective feedback to each other during communicative peer interaction activities. Pre- and post-tests were administered and statistical analyses were conducted on the change in frequency of corrective feedback and self-initiated modified output as well as in overall accuracy of spontaneous production. In Study 2, Grade 3 and 4 Canadian French immersion students were given strategy instruction to collaborate on task- and language-related problems. Their audio-recorded interactions were qualitatively analysed for contextual factors affecting peer corrective feedback. Based on the combined findings, it is concluded that language awareness can be enhanced through peer interaction but a reciprocal mindset among learners plays a significant role in deciding its outcome.
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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 | 0.039 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".