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Record W4200222103 · doi:10.14221/ajte.2021v46n10.1

The Role of Individual Preferences in the Efficacy of Written Corrective Feedback in an English for Academic Purposes Writing Course

2021· article· en· W4200222103 on OpenAlexaff
Bradley Perks, Bradley D. F. Colpitts, Matthew Michaud

Bibliographic record

Venue˜The œAustralian journal of teacher education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsCapilano University
Fundersnot available
KeywordsCorrective feedbackPsychologyMathematics educationQualitative researchTest (biology)Qualitative propertyControl (management)English as a foreign languageTreatment and control groupsMedical educationComputer scienceMedicine

Abstract

fetched live from OpenAlex

This study examined the effectiveness of written corrective and the role of individual differences (ID) in the uptake of the feedback. Data was taken from a nine-week, English as a foreign language (EFL) writing course from 101 intermediate (n=101) students at a private university in Kobe, Japan. Using an explanatory sequential mixed methods design, quantitative data was first collected concerning writing errors, followed by qualitative semi-structured interviews. Three classes were placed into either two treatment groups (direct and indirect) or a control group, and completed four writing tasks (pre-test, post-test and two delayed post-tests). The study found the two treatment groups showed significant improvements on local and global errors, whereas the control group did not. Additionally, the qualitative component elicited the influence of affective factors. The study adds to the body of literature addressing the impact of written corrective feedback, specifically on students’ self-editing strategies.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.194
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.052
GPT teacher head0.394
Teacher spread0.342 · 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 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

Citations8
Published2021
Admission routes1
Has abstractyes

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