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Record W2729356429

A Study of International Students Enrolled in the Master of Education Program at a Canadian University: Experiences, Challenges, and Expectations

2017· article· en· W2729356429 on OpenAlexaffabout
George Zhou, Tian Liu, Glenn Rideout

Bibliographic record

Venue2017 Conference of the Canadian Society for the Study of Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCurriculumMedical educationGraduate studentsFocus groupStudy abroadInternational educationPerceptionPedagogyPsychologyHigher educationPolitical scienceSociologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

Even though more and more studies have been reported in the literature about international undergraduate students’ learning experiences in North America, little research has been done to study international graduate students on North American campuses. The university where this study took place has recently established a Master of Education program for international student cohorts. This study was designed to investigate the adaption of the international graduate students who were enrolled in the MED program with a focus on their learning experiences, perceptions of the challenges they faced, and the coping strategies they used to facilitate their academic achievements. The study employed a mix-methods design, combining survey and interview. Data analysis reveals that while international graduate students shared some common challenges with international undergraduate students such as language and cultural challenges, they had their unique perspectives and expectations on curriculum and pedagogy. Suggestions for curriculum development for international graduate students will be highlighted.

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)0.006
metaresearch head score (Gemma)0.009
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 score0.982
Threshold uncertainty score0.776

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0250.006
Scholarly communication0.0070.003
Open science0.0020.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.100
GPT teacher head0.369
Teacher spread0.269 · 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
Published2017
Admission routes2
Has abstractyes

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