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Record W1991701997 · doi:10.1075/lia.1.2.07lys

Interactional feedback as instructional input

2010· article· en· W1991701997 on OpenAlexaff
Roy Lyster, Kazuya Saito

Bibliographic record

VenueLanguage Interaction and Acquisition · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsMcGill University
Fundersnot available
KeywordsSet (abstract data type)Corrective feedbackVariety (cybernetics)PsychologyPeer feedbackSecond-language acquisitionCognitive psychologyComputer scienceMathematics educationLinguisticsArtificial intelligence

Abstract

fetched live from OpenAlex

This article reports on an increasing number of SLA studies showing that interactional feedback plays a significant role in improving classroom learners’ use of the target language. Whereas the provision of feedback has proven more effective than no feedback, there are still many variables that mediate the effectiveness of interactional feedback. This article synthesizes a set of classroom studies about interactional feedback taking into account four mediating variables: (a) feedback types, (b) instructional setting, (c) learners’ age, and (d) linguistic targets. The synthesis leads to the conclusion that prescriptions to use only “implicit negative feedback” at the expense of other more overt types of interactional feedback are not supported by classroom research. The article closes with a recommendation for teachers to adopt a wide variety of interactional feedback techniques in accordance with a range of contextual, individual, and linguistic variables.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.002

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 head0.009
GPT teacher head0.264
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations27
Published2010
Admission routes1
Has abstractyes

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