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Record W4414511769 · doi:10.5539/ijel.v15n5p48

Impact of a Feedback-Literacy-Oriented Writing Intervention on EFL Learners

2025· article· en· W4414511769 on OpenAlexvenueno aff

Bibliographic record

VenueInternational Journal of English Linguistics · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
FundersChengdu UniversityChengdu University of Information Technology
KeywordsIntervention (counseling)LiteracyEnglish as a foreign languageEmpirical researchKey (lock)English language

Abstract

fetched live from OpenAlex

In L2 writing research, student feedback literacy has been increasingly recognized as a crucial construct. However, empirical studies on how students can benefit from a domain-specific approach to feedback literacy development remains scarce. This study aims to address this issue by examining the effects of a feedback-literacy-oriented writing intervention in an English as a Foreign Language (EFL) writing course. The study involved a group of 50 Chinese undergraduates and employed a mixed-method approach, collecting data through questionnaires, students’ drafts and semi-structured interviews. The study found that the intervention had a positive impact on students’ writing performance and writing self-efficacy. It was also found that the intervention was well-perceived by the students. These findings highlight the benefits of an L2 writing intervention emphasizing feedback literacy development and identify a few key elements within the approach that contribute to its success. The study concludes by discussing the pedagogical implications of the findings for EFL writing instruction.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.334
Teacher spread0.317 · 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 designNon-randomized trial
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

Citations1
Published2025
Admission routes1
Has abstractyes

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Same venueInternational Journal of English LinguisticsSame topicEFL/ESL Teaching and LearningFrench-language works237,207