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Record W4402573489 · doi:10.13187/jare.2024.2.187

Voices and Visions: An Appreciative Inquiry into International Master's Students' Ideal Learning Experience at a Southern Ontario University

2024· article· en· W4402573489 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Advocacy, Research and Education/Journal of advocacy, research and education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsVisionAppreciative inquiryIdeal (ethics)PedagogySociologyMathematics educationPsychologyEpistemologyPhilosophyAnthropology

Abstract

fetched live from OpenAlex

In this study, the researcher used the Appreciative Inquiry's dream phase to investigate international master's students' ideal learning experiences at a Canadian university.The research question was, "How do international students describe their ideal experience in terms of quality and learning experiences in a master's program?"Findings revealed a strong desire for an education that blended theoretical knowledge with practical skills.Key themes included the importance of fair tuition fees, meaningful interaction with local students, and comprehensive career development opportunities.These conditions were identified as pivotal to a transformative educational journey.The study findings urge educators and policymakers to focus on international students' well-being and future employability and highlight the multifaceted benefits for students, institutions, and broader international relations.The insights gathered underscore the potential of a student-centered academic approach in shaping an educational landscape that responds to the aspirations and needs of international graduate students.

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)0.010
metaresearch head score (Gemma)0.013
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 score0.730
Threshold uncertainty score0.536

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0420.021
Scholarly communication0.0210.006
Open science0.0030.018
Research integrity0.0030.011
Insufficient payload (model declined to judge)0.0050.001

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 head0.095
GPT teacher head0.466
Teacher spread0.371 · 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
Published2024
Admission routes1
Has abstractno

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