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Record W4391027447 · doi:10.5430/wjel.v14n2p260

Written Corrective Feedback in EFL Context: Contextual and Individual Factors Influencing Students’ Responses

2024· article· en· W4391027447 on OpenAlexvenueno aff
Nada A Alkhalaf, Ahmad I Alhojailan

Bibliographic record

VenueWorld Journal of English Language · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
FundersQassim University
KeywordsCorrective feedbackContext (archaeology)PsychologyVariation (astronomy)English as a foreign languageMathematics educationForeign language

Abstract

fetched live from OpenAlex

This study explores the written corrective feedback (WCF) behaviors of English as a foreign language (EFL) students in an EFL context and the individual and contextual factors that shape these behaviors. Ten students from the English language and translation department were interviewed and their WCF behaviors were explored. The participants were aged between 19 and 23 years, and had studied at least two writing courses taught by different instructors at a Saudi University. The findings revealed that most students had positive affective attitudes toward WCF. Most of them appreciated receiving both positive and negative comments, as the former were encouraging, while the latter helped improve their writing skills. Students’ behavioral responses to WCF varied; while some students were eager to read the feedback and correct their errors, others could correct them only when a second draft was requested. Additionally, most students relied on instructors for error correction, while only a few attempted more autonomous approaches to correction using textbooks or electronic resources. This variation in behavior could be attributed to the students’ different levels of motivation to improve their writing skills and their different individual goals for the writing course. Moreover, students’ responses to WCF were influenced by contextual factors such as the type of WCF received, the instructor’s professionalism and relationship with the students, number of assignments, and time constraints.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.024
GPT teacher head0.337
Teacher spread0.313 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2024
Admission routes1
Has abstractyes

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