MétaCan
Menu
Back to cohort
Record W4385501985 · doi:10.1111/flan.12710

Teachers' perspectives on pedagogy in short‐term language study abroad

2023· article· en· W4385501985 on OpenAlexaff
Brett Fischer, Danielle Viens

Bibliographic record

VenueForeign Language Annals · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsCégep du Vieux MontréalCégep André Laurendeau
Fundersnot available
KeywordsTRIPS architecturePedagogyExperiential learningCurriculumStudy abroadMathematics educationTerm (time)PsychologyGrounded theoryVariety (cybernetics)Teaching methodConstructivist teaching methodsLanguage educationSociologyQualitative researchComputer scienceSocial science

Abstract

fetched live from OpenAlex

Abstract Study abroad (SA) in North America is changing in two ways: short‐term trips are becoming more popular, and more students are traveling in teacher‐facilitated groups. These changes raise questions about how teaching methods can help to improve outcomes in short stays abroad, particularly in the case of language learners. To better understand teachers' perspectives on pedagogy, we conducted a series of group and individual interviews with 18 college teachers who facilitate short‐term language SA. The results of a constructivist grounded theory analysis showed that teachers believed pedagogy in short‐term SA could be improved by integrating the SA program into the at‐home curriculum, by targeting both measurable and process‐based objectives, by adopting a variety of teaching strategies including experiential teaching, and by integrating interactions between students and locals in different ways.

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)0.005
metaresearch head score (Gemma)0.007
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.006
Scholarly communication0.0060.002
Open science0.0010.005
Research integrity0.0010.004
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 head0.057
GPT teacher head0.460
Teacher spread0.403 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueForeign Language AnnalsSame topicInternational Student and Expatriate ChallengesFrench-language works237,207