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Record W2034486395 · doi:10.5539/elt.v6n4p64

EFL Students’ Reflections on Peer Scaffolding in Making a Collaborative Oral Presentation

2013· article· en· W2034486395 on OpenAlexvenueno aff
Minh Hue Nguyen

Bibliographic record

VenueEnglish Language Teaching · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyZone of proximal developmentPeer feedbackScaffoldMathematics educationPresentation (obstetrics)Sociocultural evolutionEnglish as a foreign languagePedagogyComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.036
GPT teacher head0.357
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations56
Published2013
Admission routes1
Has abstractyes

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