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Record W3047873682 · doi:10.32674/jis.v10i2.1849

Variables Affecting the Retention Intentions of Students in Higher Education Institutions

2020· article· en· W3047873682 on OpenAlexaff
Matti Haverila, Kai Haverila, Caitlin McLaughlin

Bibliographic record

VenueJournal of International Students · 2020
Typearticle
Languageen
FieldNursing
TopicHealthcare Education and Workforce Issues
Canadian institutionsConcordia UniversityThompson Rivers University
Fundersnot available
KeywordsPsychologyHigher educationInstitutionSocial psychologyQuality (philosophy)Political scienceSociologySocial science

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the differences between domestic and international students with regards to the variables affecting students’ retention intentions. Altogether, 15 variables related to retention intentions were examined and significant differences were found in six of these variables. Variables related to personal issues (e.g., medical or family difficulties) were of equal importance to both groups, while the importance of institution and performance-centric variables differed between the groups. Social integration, ineffective study skills, difficulty adjusting to college life, poor extracurricular activities, and poor housing arrangements were perceived to be significantly more important by international students, while poor quality of instruction was perceived to be significantly more important by domestic students. Thus, international and domestic students require different retention strategies on the part of the institution.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.102
GPT teacher head0.440
Teacher spread0.337 · 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 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

Citations52
Published2020
Admission routes1
Has abstractyes

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