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Record W4408130173 · doi:10.32674/sa2mgs78

Culture Difference: Perceptions of Student Engagement of Chinese International Graduate Students at a Canadian University

2025· article· en· W4408130173 on OpenAlexaboutno aff
Meng Xiao

Bibliographic record

VenueJournal of Comparative & International Higher Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsStudent engagementGraduate studentsPerceptionPsychologyMedical educationSignificant differenceMathematics educationPedagogyMedicineInternal medicine

Abstract

fetched live from OpenAlex

This article provides the perspectives of Chinese international graduate students about student engagement while attending a university in Canada. The prominent Western perception of student engagement in Canadian universities emphasizes student-oriented and active-participation to hopefully enhance student academic success and better social engagement. However, this Western perspective fails to recognize the broader perceptions of student engagement from a global perspective. The Confucian perspective of student engagement highlights teacher-oriented and deep-thinking engagement through academic achievement and exam-oriented activities. This study found that the student and staff participants have varied perceptions of student engagement based on their prior experiences and cultural backgrounds. However, these different ways of perceiving student engagement are not well acknowledged in Canadian higher education. Chinese international graduate students need to understand the different perceptions of student engagement between Western and Confucian cultures, and they need to enhance their student engagement based on their experiences and cultural backgrounds.

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)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.497

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.004
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.002
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 head0.067
GPT teacher head0.442
Teacher spread0.374 · 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
Published2025
Admission routes1
Has abstractyes

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