Effects of proficiency differences and patterns of pair interaction on second language learning: collaborative dialogue between adult ESL learners
Bibliographic record
Abstract
This study investigated the effects of second language (L2) proficiency differences in pairs and patterns of interaction on L2 learning, making use of both qualitative and quantitative data. We designed the study in such a way that four different core participants interacted with higher and lower proficiency non-core participants. These learners engaged in a three-stage task involving pair writing, pair comparison (between their original text and a reformulated version of it) and individual writing. The core participants also engaged in a stimulated recall after the task. We analysed each pair's collaborative dialogue in terms of language-related episodes and patterns of pair interaction (Storch, 2002a) as well as each learner's individual post-test score. The findings suggested that the patterns of pair interaction greatly influenced the frequency of LREs and post-test performance. When the learners engaged in collaborative patterns of interaction, they were more likely to achieve higher posttest scores regardless of their partner's proficiency level. It seems that proficiency differences do not necessarily affect the nature of peer assistance and L2 learning.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".