MétaCan
Menu
Back to cohort
Record W3181733821 · doi:10.1111/ijal.12378

ESL learners’ perception and its relationship with the efficacy of written corrective feedback

2021· article· en· W3181733821 on OpenAlexaff
Tara Shankar Sinha, Hossein Nassaji

Bibliographic record

VenueInternational Journal of Applied Linguistics · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCorrective feedbackPerceptionPsychologyNarrativeGroup (periodic table)Control (management)Mathematics educationComputer scienceLinguisticsArtificial intelligenceChemistry

Abstract

fetched live from OpenAlex

Abstract This study investigated the effects of direct and indirect WCF on learners’ accuracy in revision and new pieces of writing and also examined the relationship between learners’ perception of WCF and the effectiveness of each feedback type. Data were collected from 56 learners divided into three groups: direct WCF group ( n = 18), indirect WCF group ( n = 18) and control group ( n = 20). All groups produced a narrative text based on picture prompts, revised the same text, and produced a new text. The two treatment groups also completed a feedback perception questionnaire. The results demonstrated that the two feedback groups significantly outperformed the control group in both revision and new pieces of writing, but no significant relationship was found between learners’ perception and the effectiveness of either feedback types.

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)0.003
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.269
Teacher spread0.239 · 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 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

Citations29
Published2021
Admission routes1
Has abstractyes

Explore more

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