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Record W3110228380 · doi:10.18806/tesl.v37i2.1334

Learner Personality and Response to Oral Corrective Feedback in an English for Academic Purposes Context

2020· article· en· W3110228380 on OpenAlexfundvenueno aff
Alina Lemak, Antonella Valeo

Bibliographic record

VenueTESL Canada Journal · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
FundersUniversity of TorontoYork University
KeywordsPsychologyAgreeablenessBig Five personality traitsPersonalityContext (archaeology)Corrective feedbackPersonality psychologyExtraversion and introversionSocial psychologyMathematics education

Abstract

fetched live from OpenAlex

Corrective feedback (CF) is an important part of effective instruction, and a rich body of research has investigated how best to implement various CF strategies and approaches. Researchers have increasingly become aware of individual difference that have an impact on the effect and effectiveness of CF. One area of individual difference that has been shown to influence learning outcomes is personality; yet, findings have been inconsistent, and the influence of learners’ personality traits on oral CF effectiveness has largely been neglected. This study aimed to fill this gap by investigating how learners with different personalities experience and benefit from different types of oral CF. Situated in an intact class of adult language-learners in an academic context, data collection included a Five Factor Model (FFM) personality test, video and audio recordings of classroom activities, and individual interviews/stimulated-recall sessions. The findings of this study suggest that personality traits do appear to play a role in how students experience CF. Relationships between global FFM personality traits and CF response emerged: competitiveness and perfectionism characteristics appeared influential and a possible interaction between agreeableness and neuroticism is discussed. Pedagogical implications are suggested. La rétroaction corrective est une partie importante de l’instruction efficace, et un important corpus de recherche s’est penché sur la meilleure façon de mettre en place des stratégies et des approches de rétroaction corrective. Les chercheurs sont devenus de plus en plus conscients des différences individuelles qui ont un impact sur l’effet et sur l’efficacité de la rétroaction corrective. On a montré qu’une aire de différence individuelle, la personnalité, influence les résultats d’apprentissage; cependant, les résultats ne sont pas uniformes, et l’influence des traits de personnalité des apprenants sur l’efficacité de la rétroaction corrective orale a été pour la plupart négligée. Le but de la présente étude était de combler cette lacune en étudiant comment des apprenants de différente personnalité vivent et bénéficien de différents types de rétroactions correctives orales. Située dans une classe intacte d’apprenants adultes de langues dans un contexte universitaire, la collecte de données comprenait un test de personnalité se basant sur le modèle en cinq facteurs (MCF), des enregistrements audio et vidéo des activités de classe, ainsi que des séances d’entrevues individuelles/de rappels stimulés. Les résultats de cette étude suggèrent que les traits de personnalité semblent bien jouer un rôle dans la façon dont les étudiants perçoivent la rétroaction corrective. Les relations entre les traits de personnalité généraux MCF et la réaction à la rétroaction corrective ont montré que : les caractéristiques de compétitivité et de perfectionnisme semblaient jouer un rôle marquant et on discute d’une interaction possible entre l’agréabilité et le neuroticisme. On suggère des implications pédagogiques.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.072
GPT teacher head0.281
Teacher spread0.208 · 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
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

Citations2
Published2020
Admission routes2
Has abstractyes

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