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Record W4392197046 · doi:10.5539/ells.v14n1p30

Students’ Beliefs Toward the Effectiveness of Receiving Written Corrective Feedback for Developing L2 Writing Skills

2024· article· en· W4392197046 on OpenAlexvenueno aff
Shayma Omar A. AL-Ahmadi, Hanadi Abdulrahman Khadawardi

Bibliographic record

VenueEnglish Language and Literature Studies · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
FundersKing Abdulaziz University
KeywordsCorrective feedbackComputer scienceMathematics educationPsychology

Abstract

fetched live from OpenAlex

This study investigated students’ beliefs and preferences regarding written corrective feedback (WCF) in an EFL environment. More specifically, it examined Saudi students’ perceptions of the utility and effect of WCF in enhancing their language skills at KAU. The study used a mixed-method research paradigm: a qualitative part, where data were collected from semi-structured interviews, and a quantitative part, which involved a closed-ended questionnaire to investigate participants’ beliefs regarding WCF and how it is used in EFL writing classes. The results can assist language teachers and curriculum designers in adapting feedback systems suitable to students’ expectations and learning styles. Finally, the study aims to bridge the gap between the current instructional approaches and student preferences.

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)0.006
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.296
Teacher spread0.282 · 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 designObservational
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
Published2024
Admission routes1
Has abstractyes

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