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Record W3109962707 · doi:10.18806/tesl.v37i2.1339

Dynamic Written Corrective Feedback among Graduate Students: The Effects of Feedback Timing

2020· article· en· W3109962707 on OpenAlexvenueno aff
Grant Eckstein, Maureen E. Sims, Lisa Rohm

Bibliographic record

VenueTESL Canada Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
FundersBrigham Young University
KeywordsCorrective feedbackFluencyGrammarPeer feedbackPsychologyCurriculumLinguisticsComputer scienceMathematics educationPedagogyPhilosophy

Abstract

fetched live from OpenAlex

Dynamic written corrective feedback (DWCF) is a pedagogical approach that offer meaningful, manageable, constant, and timely corrective feedback on student writing (Hartshorn et al., 2010). It emphasizes indirect and comprehensive writte error correction on short, daily writing assignments. Numerous studies have demonstrated that its use can lead to fewer language errors among undergraduate and pre-matriculated college writers (see Kurzer, 2018). However, the benefits of DWCF among second language (L2) graduate writers and the role of feedback timing have not been well examined. We analyzed timed writing samples over a 12-week intervention from 22 L2 graduate students who either received biweekly feedback on their writing throughout a semester, or postponed feedback until the last two weeks of the semester. Writing was analyzed for grammatical errors, lexical and syntactic complexity, and fluency. Results showed that neither timely nor postponed feedback led to significant improvement in grammatical accuracy or lexical complexity, but timely feedback did result in more fluent and complex writing. These findings suggest that the timing of feedback may be trivial for accuracy development but is more important for complexity among graduate writers. Teachers, teacher trainers, and writing administrators may use these insights as they plan curricula and design grammar and writing interventions. La rétroaction corrective écrite dynamique (RCED) est une approche pédagogique qui propose une rétroaction significative, gérable, constante et opportune sur les rédactions des étudiants (Hartshorn et al. 2010). Elle insiste sur la correction complète et indirecte d’erreurs dans de courts devoirs de rédaction quotidiens. De nombreuses études ont démontré que son utilisation peut amener les rédacteurs de premier cycle ou pré-inscrits au collège à faire moins d’erreurs de langue (voir Kurzer, 2018). Cependant, les avantages de la RCED chez les rédacteurs diplômés de seconde langue (L2) et le rôle joué par l’opportunité de la rétroaction n’ont pas été bien étudiés. Nous avons analysé des échantillons de rédaction écrites en temps limité sur une période d’intervention de 12 semaines chez 22 étudiants diplômés de L2 qui recevaient de la rétroaction deux fois par semaine sur leurs rédactions pendant la durée du semestre, ou une rétroaction différée jusqu’à deux semaines avant la fin du semestre. Les rédactions ont été analysées pour découvrir les erreurs grammaticales, la complexité lexicale et syntaxique, ainsi que la fluidité Les résultats ont montré que ni la rétroaction opportune, ni la rétraction différé ne se traduisaient par une amélioration marquée de la précision grammaticale ou de la complexité lexicale, mais la rétroaction opportune menait à une rédaction plus fluide et plus complexe. Ces résultats suggèrent que l’opportunité de la rétroaction peut ne pas beaucoup influer sur le développement de la précision, mais s’avère plus importante pour la complexité chez les rédacteurs diplômés. Les enseignants, les formateurs d’enseignants et les administrateurs de programmes de rédaction peuvent se servir de ces résultats lorsqu’ils planifient les programmes et conçoivent les interventions en grammaire et en rédaction.

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)0.005
metaresearch head score (Gemma)0.056
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.056
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.024
GPT teacher head0.305
Teacher spread0.281 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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