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Record W4406298471 · doi:10.13189/ujer.2024.120601

Exploring the Perspectives of Online Study Abroad Programs in Japan during the COVID-19 Pandemic: A Narrative Approach

2024· article· en· W4406298471 on OpenAlexaboutno aff
Yukiyo Nishida, Atsuko Watanabe, Masuko Miyahara, Maki Ojima, Gaby Benthien, Kazuko Takano

Bibliographic record

VenueUniversal Journal of Educational Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicNarrative2019-20 coronavirus outbreakStudy abroadSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychologySociologyMathematics educationHistoryPedagogyVirologyMedicineLiteratureArt

Abstract

fetched live from OpenAlex

COVID-19 profoundly impacted countries worldwide between early 2020 and late 2021 due to travel restrictions, lockdowns, and health risks. Many higher education institutions responded to the health threat posed by COVID-19 by moving their on-campus programs online. Study abroad programs also needed to undergo adjustments, with some universities establishing alternative arrangements. This study uses a narrative approach to examine Online In-Country Study Abroad Program (ONIC-SAPs) from the perspective of Japan-based participants, academic, and administrative staff who were involved and participated. The program was operated by a university in Japan in collaboration with a partner university in Canada and a global language school during the height of the pandemic. The findings highlight that the advantages of ONIC-SAPs are also seen as the disadvantages of the programs. Acquiring language skills and experiencing intercultural encounters with the aid of technology may become even more common in the future, yet the extent to which this kind of learning is of benefit to all concerned should be considered. The study highlights that there is a need for further research into the content and curriculum of ONIC-SAPs to find ways to improve the value of online learning for all stakeholders, especially for study abroad programs. Although the participants were recruited from the Japanese side only, the findings will resonate with study abroad stakeholders and scholars of study abroad programs worldwide.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.037
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.387
GPT teacher head0.509
Teacher spread0.122 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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