EFL Students’ Reflections on Peer Scaffolding in Making a Collaborative Oral Presentation
Bibliographic record
Abstract
Informed by sociocultural theory and previous research on peer scaffolding in second language (L2) learning, which largely focuses on collaborative writing in English as a second language (ESL) contexts, this study investigates the ways in which Vietnamese English as a foreign language (EFL) students provide peer scaffolding to each otherduring a collaborative presentation task and how they benefit from this experience. Data were collected from 12 participants through reflective reports and interviews. Content analysis of data suggests six categories of peer scaffolding behaviours among the students, namely workload sharing, pooling ideas and resources, technology support, peer feedback, support in answering the audience’s questions, and affective support and the benefits that the students gained from them. The findings demonstrate that collaborative pair work creates learning conditions where peers provide mutual help, which supports previous research findings. The identified peer scaffolding behaviours also show important features suggested in the literature. Although peer scaffolding has been largely studied in L2 writing, it remains a new area of research in L2 speaking discourse. This study extends the literature to this under-researched area and offers a number of pedagogical and theoretical implications based on the findings.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".