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Record W4310224264 · doi:10.5430/wjel.v12n7p284

Exploring L2 Written Corrective Feedback in the Saudi Context: A Critical Review

2022· review· en· W4310224264 on OpenAlexvenueno aff
Fahad Alqurashi

Bibliographic record

VenueWorld Journal of English Language · 2022
Typereview
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)Context (archaeology)Corrective feedbackDiversity (politics)Computer scienceFunction (biology)PsychologyKnowledge managementMathematics educationPolitical science

Abstract

fetched live from OpenAlex

Written corrective feedback (WCF) has received increased attention in recent years because of its widespread implementation and also due to the philosophical debate around its nature, function, and potential benefits. The purpose of this study is to examine the areas that demonstrate development in terms of scope when it comes to WCF in the Saudi context. The study analyses thirty publications that were published between 2016 and 2021 and retrieved from Semantic Scholar and Google Scholar, to assess the key findings on three dimensions: (1) effectiveness of WCF; (2) comparative effectiveness of different strategies in WCF; and (3) beliefs and attitudes of students/teachers toward WCF. Application of the PRISMA 2020 approach shows that most studies on WCF in Saudi Arabia investigated the beliefs and attitudes of students and teachers towards WCF followed by the effectiveness of WCF for learners, and comparative effectiveness of different strategies in WCF to students. The outcomes of this study indicate that a number of difficulties surrounding the scope of feedback need researchers' attention, and that there is also a need to address the existing technique imbalance by including a greater diversity of research procedures in future research designs.

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)0.006
metaresearch head score (Gemma)0.008
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.875
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.002
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 head0.148
GPT teacher head0.405
Teacher spread0.257 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2022
Admission routes1
Has abstractyes

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