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Record W2770053537 · doi:10.5430/ijhe.v6n6p66

Acculturative Stress and Disengagement: Learning from the Adjustment Challenges faced by East Asian International Graduate Students

2017· article· en· W2770053537 on OpenAlexvenueno aff
Dawn Lyken‐Segosebe

Bibliographic record

VenueInternational Journal of Higher Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsAcculturationDisengagement theoryTest of English as a Foreign LanguageGraduate studentsPsychologyEliteStudy abroadAcademic achievementInternational educationMedical educationHigher educationPedagogyMathematics educationPolitical scienceLanguage educationImmigrationMedicineGerontology

Abstract

fetched live from OpenAlex

International graduate students meet TOEFL, GPA, and other admissions criteria to gain entry into US colleges and universities. During their stay in the USA, they provide educational and economic contributions for their host country. In contrast to their educational and economic potential, international students often demonstrate poor academic and social integration at their host institutions. Grounded theory methodology was used to investigate what accounts for the academic, cultural, and social adjustment problems faced by East Asian graduate students pursuing studies at an elite private not-for-profit university in the USA. Findings revealed that students experienced lowered self-confidence and acculturative stress as a result of challenges encountered during their first year, language barriers, different teaching styles and teaching environments, and their interactions with professors. Raising faculty sensitivity to cultural differences among international students, early adjustment counseling and obtaining regular feedback are recommended.Keywords: college student experience, student engagement, international students, adjustment, acculturative stress

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.000
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 head0.087
GPT teacher head0.410
Teacher spread0.323 · 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 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

Citations19
Published2017
Admission routes1
Has abstractyes

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