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Record W2790433851 · doi:10.1177/0033688217746205

Drama for L2 Speaking and Language Anxiety: Evidence from Brazilian EFL Learners

2018· article· en· W2790433851 on OpenAlexaff
Angelica Galante

Bibliographic record

VenueRELC Journal · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDramaAnxietyPsychologyForeign language anxietyForeign languagePedagogyLiteratureArt

Abstract

fetched live from OpenAlex

Anxiety is a dimension of L2 speaking that has been heavily investigated over the past several decades, but there is a paucity of research investigating instruction aiming at lowering anxiety. While research suggests drama lowers L2 learners’ anxiety, it is unclear to what extent anxiety is affected by drama. This article reports results from a mixed methods study examining whether drama impacts foreign language anxiety (FLA). The participants were 24 Brazilian adolescents who took part in two distinct four-month EFL programmes: a drama and a non-drama programme. An adapted version of the Foreign Language Classroom Anxiety Scale (FLCAS) was used as pre- and post measures. Analyses from FLCAS indicate a significant reduction in FLA levels among learners in both groups over time, with a slightly better improvement among learners in the drama group. Further analysis provides evidence that drama can enhance comfort levels when speaking the L2. Implications for research and language teaching are discussed.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.299
Teacher spread0.260 · 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

Citations61
Published2018
Admission routes1
Has abstractyes

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