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Record W4416347416 · doi:10.53761/9chx5n75

How Can International Students be Supported in Post-Secondary Education in Canada? A Qualitative Study

2025· article· W4416347416 on OpenAlexaffabout
Claudia Sasse

Bibliographic record

VenueJournal of University Teaching and Learning Practice · 2025
Typearticle
Language
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsAmbrose University
Fundersnot available
KeywordsFocus groupQualitative researchAcculturationCultural competenceIntercultural competenceCompetence (human resources)Higher educationConfusionPerceptionInternational education

Abstract

fetched live from OpenAlex

International students may encounter culture shock, a state of confusion or disorientation that arises as they adapt to an unfamiliar culture and leave behind their familiar one (Amos & Lordly, 2014). Since international students in Canada come from diverse countries, their perceptions of post-secondary education can vary greatly, often leading to academic challenges. This qualitative study, drawing on Acculturation Theory, explores how post-secondary institutions can more effectively address international students' needs. Data was collected through four focus group interviews with faculty, staff, and international students at a Western Canadian post-secondary institution. Students shared personal and academic needs while faculty and staff discussed their awareness of these needs and resource utilization. Findings emphasized the importance of promoting intercultural competence and personalizing learning experiences. Faculty and staff expressed a need for additional training and resources. The key implication is that effective support requires institutions to understand students' home-country educational perspectives and provide appropriate campus resources.

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 score0.945
Threshold uncertainty score0.397

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0260.010
Scholarly communication0.0070.002
Open science0.0030.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.019
GPT teacher head0.372
Teacher spread0.354 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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