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Record W2097765129 · doi:10.18806/tesl.v31i0.1185

An Exploratory Study into Trade-off Effects of Complexity, Accuracy, and Fluency on Young Learners’ Oral Task Repetition

2015· article· en· W2097765129 on OpenAlexvenueno aff
Evelyn Sample, Marije Michel

Bibliographic record

VenueTESL Canada Journal · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsFluencyMirroringRepetition (rhetorical device)Task (project management)PsychologyTask analysisCognitive psychologyLinguisticsMathematics educationSocial psychology

Abstract

fetched live from OpenAlex

Studying task repetition for adult and young foreign language learners of English (EFL) has received growing interest in recent literature within the task-based approach (Bygate, 2009; Hawkes, 2012; Mackey, Kanganas, & Oliver, 2007; Pinter, 2007b). Earlier work suggests that second language (L2) learners benefit from repeating the same or a slightly different task. Task repetition has been shown to enhance fluency and may also add to complexity or accuracy of production. However, few investigations have taken a closer look at the underlying relationships between the three dimensions of task performance: complexity, accuracy, and fluency (CAF). Using Skehan’s (2009) trade-off hypothesis as an explanatory framework, our study aims to fill this gap by investigating interactions among CAF measures. We report on the repeated performances on an oral spot- the-difference task by six 9-year-old EFL learners. Mirroring earlier work, our data reveal significant increases of fluency through task repetition. Correlational analyses show that initial performances that benefit in one dimension come at the expense of another; by the third performance, however, trade-off effects disappear. Further qualitative explanations support our interpretation that with growing task-familiarity students are able to focus their attention on all three CAF dimensions simultaneously.Au sein de la littérature relative à l’approche fondée sur les tâches, on évoque de plus en plus d’études portant sur la répétition des tâches pour l’enseignement de l’anglais langue étrangère aux jeunes et aux adultes (Bygate, 2009; Hawkes, 2012; Mackey, Kanganas, & Oliver, 2007; Pinter, 2007b). Des études antérieures semblent indiquer que les apprenants en L2 profitent de la répétition de la même tâche ou d’une tâche légèrement différente. Il a été démontré que la répétition des tâches améliore la fluidité et qu’elle pourrait augmenter la complexité ou la précision de la production. Toutefois, peu d’études se sont penchées davantage sur les relations sous-jacentes entre les trois dimensions de l’exécution des tâches : la complexité, la précision et la fluidité. S’appuyant sur l’hypothèse du compromis de Skehan (2009) comme cadre explicatif, notre étude vise à combler cette lacune en examinant les interactions entre les mesures de ces trois éléments. Nous faisons rapport du rendement de six jeunes âgés de 9 ans qui apprennent l’anglais comme langue étrangère alors qu’ils répètent une tâche impliquant l’identification de différences. Nos données reproduisent les résultats de travaux antérieurs en ce qu’elles révèlent une amélioration significative de la fluidité par la répétition de tâches. Des analyses corrélationnelles indiquent que l’amélioration d’une dimension lors des exécutions initiales se fait aux dépens d’une autre; cet effet de compromis disparait, toutefois, à la troisième exécution. Des explications quali- tatives supplémentaires viennent appuyer notre interprétation selon laquelle la familiarité croissante que ressentent les élèves avec une tâche leur permet de se concentrer sur les trois dimensions (complexité, précision et fluidité) à la fois.

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)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.056
GPT teacher head0.279
Teacher spread0.223 · 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

Citations183
Published2015
Admission routes1
Has abstractyes

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