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Record W2495582910 · doi:10.5539/ies.v9n8p36

Examining the Effects of Financial Aid on Student Persistence in Taiwanese Higher Education

2016· article· en· W2495582910 on OpenAlexvenueno aff
Ching-Hui Lin

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPersistence (discontinuity)InstitutionHigher educationPerspective (graphical)Mathematics educationMedical educationPedagogySociologyEconomic growthEconomicsSocial science

Abstract

fetched live from OpenAlex

<p class="apa">The purpose of this study was to examine the effects of financial aid policies on student persistence between the first and second year at a private four-year postsecondary institution in Taiwan. A two-phase sequential research design was employed with priority was given to the quantitative data–structural equation modeling (SEM). While the conceptual framework of this study was drawn from multiple research antecedents in relation to student persistence the major perspective guiding it was based on St. John’s (1992) analysis of the research. Overall, the greatest effects on persistence were the measures of high school academic performance, followed by campus work, academic integration, social aspiration for attending college, financial aid, and parental supports, while neither parental educational attainments, faculty-student interactions, the practical value for future employment, nor peer relations were associated with students’ decision to remain enrolled in college, results which merit further investigation. In sum, this study provides a necessary beginning step, more institutional research is needed in Taiwan to improve policy makers’ and institutional researchers’ understanding of the complex interplay between student financial aid and college experiences in students’ decisions to persist in college as well as to develop a longitudinal database to identify ways to increase student success.</p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.254
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.081
GPT teacher head0.457
Teacher spread0.377 · 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 teacher head, 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

Citations8
Published2016
Admission routes1
Has abstractyes

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