Identifying Linguistic Markers of Collaboration in Second Language Peer Interaction: A Lexico‐grammatical Approach
Bibliographic record
Abstract
Although there is consensus that collaboration refers to two or more learners working together to accomplish a task (Davin & Donato, 2013; Ohta, 2001), debate remains about how to assess collaboration. Researchers have pursued two approaches to evaluate collaboration during peer interaction: rater judgments (e.g., Ahmadi & Sedeghi, 2016; Winke, 2013) and qualitative coding of interactional patterns (e.g., Galaczi, 2008; Storch, 2002a). Largely absent, however, has been any attempt to describe the linguistic features of collaboration. Therefore, the present study uses corpus linguistic techniques to identify the linguistic markers of collaborative and noncollaborative peer interactions. Students of English as a second language ( N = 80) enrolled in an intensive English program carried out a paired oral test as part of the program's formative assessment procedures. Their interactions were audio‐recorded and rated using an analytic rubric with three categories (collaboration, task completion, and style), and transcripts were analyzed for 146 linguistic features using the Biber Tagger (Biber, 1988). Linguistic features associated with high collaboration included first‐ and second‐person pronouns, wh ‐questions, that deletion, and subordinate conjunctions, whereas low‐collaboration interactions were characterized by nominal forms. The collaborative and noncollaborative functions served by these linguistic features 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 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".