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Record W4224223244 · doi:10.1075/sar.21006.mat

The impact of predeparture instruction on pragmatic development during study abroad

2022· article· en· W4224223244 on OpenAlexaboutno aff
Shoichi Matsumura

Bibliographic record

VenueStudy Abroad Research in Second Language Acquisition and International Education · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
FundersRyukoku University
KeywordsRealization (probability)Test (biology)Intervention (counseling)Context (archaeology)Study abroadMathematics educationPsychologyMetacognitionInductive methodPedagogyLinguisticsTeaching methodCognitionMathematics

Abstract

fetched live from OpenAlex

Abstract This study investigates the impact of a predeparture intervention on developing pragmatic knowledge in a study abroad context. The study included 66 university-level Japanese learners of English who participated in a four-month study abroad program in Canada. The intervention consisted of the implicit-inductive, explicit-deductive, and explicit-inductive methods of instruction on speech acts. Pragmatic development was measured by gain scores on a written a discourse completion test requiring realization of apologies. Results of the analysis of covariance, controlling for levels of English proficiency, revealed that the explicitly taught groups had significantly larger gains in pragmatic knowledge than the implicitly taught group, and that when comparing the deductive and inductive approaches in the explicit instruction, the two groups did not differ significantly. Follow-up interviews using extreme-case sampling revealed that the metacognitive strategies they had acquired at the predeparture stage contributed to the gains. Implications for maximizing pragmatic development during study abroad 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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.422
Teacher spread0.383 · 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 designQualitative
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

Citations14
Published2022
Admission routes1
Has abstractyes

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