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Record W3133547010 · doi:10.1017/9781108589789.010

Oral Corrective Feedback

2021· book-chapter· en· W3133547010 on OpenAlexaff
Rhonda Oliver, Rebecca Adams

Bibliographic record

VenueCambridge University Press eBooks · 2021
Typebook-chapter
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsCarleton UniversityUniversity of Victoria
Fundersnot available
KeywordsCorrective feedbackPsychologyProcess (computing)Cognitive psychologyComputer scienceMathematics education

Abstract

fetched live from OpenAlex

We provide an overview of research that explains what oral corrective feedback is, how it can be expressed by teachers and peers, and how it may impact the language development process. We define oral corrective feedback as a negative evidence provided in response to learner error in an oral mode. A theoretical rationale for the role of feedback is described, drawing on research from both cognitive-interactionist and sociocultural explanations of second language learning through oral communication. Examples from numerous studies are incorporated to exemplify the range of ways feedback is provided on different types of linguistic errors. Research on the relative effectiveness of different types of feedback is reviewed, as well as empirical inquiry into the role of individual and social factors that can enhance or limit the effectiveness or oral feedback, concluding that oral corrective feedback is an important factor for language learning in instructed settings. We close with recommendations for research-driven teaching practice with respect to oral corrective feedback, cautioning that teachers need to consider learner experiences and expectations of feedback, their pedagogical objectives and approach, as well as learners developmental needs, self-monitoring skills, and ability to provide feedback to one another.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.896
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.201
Teacher spread0.159 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations16
Published2021
Admission routes1
Has abstractyes

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