MétaCan
Menu
Back to cohort
Record W2532554194 · doi:10.5539/elt.v9n11p85

Corrective Feedback in SLA: Theoretical Relevance and Empirical Research

2016· article· en· W2532554194 on OpenAlexvenueno aff
Jin Chen, Jianghao Lin

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
FundersNational Social Science Fund of ChinaGuangdong University of Foreign Studies
KeywordsCorrective feedbackSecond-language acquisitionPsychologyRelevance (law)Empirical researchLanguage productionCognitive psychologySociocultural evolutionLanguage acquisitionTheoretical linguisticsLanguage proficiencyCognitionLinguisticsCognitive scienceEpistemologyMathematics educationSociology

Abstract

fetched live from OpenAlex

<p>Corrective feedback (CF) refers to the responses or treatments from teachers to a learner’s nontargetlike second language (L2) production. CF has been a crucial and controversial topic in the discipline of second language acquisition (SLA). Some SLA theorists believe that CF is harmful to L2 acquisition and should be ruled out completely while others regard CF as an essential catalyst for L2 development. The last two decades have witnessed a dramatic increase in empirical research on the effectiveness of CF. This article, with an aim to provide an informed knowledge of the potential role of CF, briefly traces the history of research on CF and proposes some recommendations for further studies. It starts by surveying a range of theoretical stances on the role of error and error correction (also known as CF) in SLA. It then moves into detailed discussion of three issues on CF heatedly debated either within a cognitive or a sociocultural framework. By examining the empirical findings, some possible topics for further studies are uncovered.</p>

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)0.003
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.041
GPT teacher head0.348
Teacher spread0.307 · 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

Citations12
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueEnglish Language TeachingSame topicEFL/ESL Teaching and LearningFrench-language works237,207