The Role of Peer Support in ESL Students' Accomplishment of Oral Academic Tasks
Bibliographic record
Abstract
How do L2 students work together to accomplish their public, in-class tasks? From a sociocultural perspective (e.g., Duff, 1995; Lantolf, 2000), the present study takes a behind-the-scenes look at peer collaboration in which a group of three Japanese undergraduate students engaged to accomplish an academic presentation task during their year-long studies in a content-based ESL program at a Canadian university. Methods for data collection reflected a qualitative case study approach and included audio-recorded observations of project work, in-depth interviews, and students' journals and papers. Data showed that students' preparatory activities outside the classroom included negotiating task definition and teacher expectations, sharing experiences, collaborative dialogue (Swain, 2000) in preparing presentation materials, and rehearsing and peer-coaching. Analysis shed useful light on students' contextualization of and orientation to the task, the interdependence of spoken and written language in task preparation, and the role of the L1 as a scaffold for L2 task accomplishment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| 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".