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Record W2953777551 · doi:10.1558/isla.37949

The effects of written corrective feedback

2019· article· en· W2953777551 on OpenAlex
Khaled Karim, Hossein Nassaji

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

VenueInstructed Second Language Acquisition · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCorrective feedbackArgument (complex analysis)Key (lock)Computer sciencePsychologyEpistemologyMathematics educationPhilosophyComputer securityMedicine

Abstract

fetched live from OpenAlex

This paper presents a critical synthesis of research on written corrective feedback (WCF) and its effects on second language (L2) learning over the past four decades. WCF is an essential component of L2 teaching. However, whether WCF helps has been an issue of considerable debate in the literature. While many researchers have stressed its importance, some others have expressed doubt concerning its effectiveness. The controversy over the effectiveness of WCF was heightened when Truscott argued in 1996 that error correction is ineffective and harmful and others who strongly countered his argument that feedback is effective (e.g. Chandler 2003; Ferris 1999). This article provides an in-depth synthesis and analysis of this area of research, examining key issues and findings as well as the various contentions and concerns raised regarding the effects of WCF. The article concludes with implications for future research and with insights about how to move forwards.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.996

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.003
GPT teacher head0.199
Teacher spread0.195 · 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