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Record W1991701997 · doi:10.1075/lia.1.2.07lys

Interactional feedback as instructional input

2010· article· en· W1991701997 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.000
metaresearch head score (Gemma)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.929
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0280.000

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 head0.009
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