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Record W2889710219 · doi:10.1017/s026144481800023x

Language learning and study abroad

2018· article· en· W2889710219 on OpenAlexaff
Christina L. Isabelli-García, Jennifer Bown, John L. Plews, Dan P. Dewey

Bibliographic record

VenueLanguage Teaching · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsPaceAgency (philosophy)Language acquisitionEmpirical researchStudy abroadCurriculumExperiential learningPedagogyLanguage educationPsychologySecond-language acquisitionMathematics educationLinguisticsSociologyEpistemologySocial science

Abstract

fetched live from OpenAlex

The aim of this review is to synthesize empirical studies on undergraduate language learners’ experience abroad during a time period of a year or less. To help provide a framework to this synthesis, we begin our review by tracing the recent evolution of empirical mixed-method research on the learner, identifying problems and characteristics that language learners generally encounter in the study abroad (SA) experience. We take a closer look at variables related to individual difference such as anxiety, motivation, and attitudes to more recent views of learner identity in language learning. We highlight the shift to language learner agency, a topic that merits more discussion in SA literature. We then review how the SA learning environments are treated. This review takes a closer look at research informed by socially grounded theories. Finally, we review the role that SA plays in undergraduate language curricula, where the objectives of the experience are aligned with at-home (AH) curricula, a topic that has not been fully discussed in SA literature. The conclusions offer suggestions for keeping pace with the broader field of applied/educational linguistics.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0050.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.014
GPT teacher head0.307
Teacher spread0.293 · 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 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

Citations175
Published2018
Admission routes1
Has abstractyes

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