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Record W4316814859 · doi:10.5430/jct.v12n1p100

Factors Affecting Adaptability of International Students in Malaysia during COVID-19 Pandemic

2023· article· en· W4316814859 on OpenAlexvenueno aff
Mengtian Xie, Walton Wider, Subashini K. Rajanthran, Ling Shing Wong, Choon Kit Chan, Siti Sarah Maidin, Nurul Aliah Mustafa

Bibliographic record

VenueJournal of Curriculum and Teaching · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAdaptabilityContext (archaeology)PandemicCoronavirus disease 2019 (COVID-19)AcculturationPsychologyPolitical scienceGeographyMedicineManagement

Abstract

fetched live from OpenAlex

It is crucial to investigate adaptability in the context of COVID-19, as evidence suggests that difficulties posed by adaptability can be exacerbated during times of crisis. International students encounter additional pressures during this period, which might impair their capacity to stay and prosper in a new place. In light of this context, the purpose of this study is to examine the factors that contribute to the adaptability of international university students in Malaysia, namely the academic system, acculturation, and social support. A cross-sectional research design was used, and the research instruments were adapted from a number of previous studies. A total of one hundred thirty online questionnaires were filled out by international students in Malaysia. The research hypotheses were evaluated using SPSS Version 27.0. All predictors were found to have a statistically significant and positive effect on the adaptability of international students. This research aims to shed light on educational management strategies for addressing the adaptability challenges faced by most international students within COVID-19 by illuminating the key drivers that influence adaptability.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.056
GPT teacher head0.408
Teacher spread0.351 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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