Examining the Effects of Financial Aid on Student Persistence in Taiwanese Higher Education
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".