Critical Factors Underlying Students’ Choice of Institution for Graduate Programmes: Empirical Evidence from Ghana
Bibliographic record
Abstract
The growth in higher education industry has caused a tremendous increase in the number and type of colleges, polytechnics and universities offering similar academic programmes especially in business disciplines in Ghana. The resultant competition in the education industry makes it crucial for education managers to understand the latent factors that underlie students’ college and programme selection. The purpose of this study was to explore the factors underlying students’ choices in accessing higher education in Ghana. The study was a cross-sectional survey of 183 students offering different masters’ programmes in a public university in Ghana. It utilized exploratory factor analysis to identify seven latent factors that play critical role in students’ choice of master’s programmes. These factors are cost, student support quality, attachment to institution, recommendation from lecturers and other staff, failure to gain alternative admissions, location benefits, among others. The results of this research are beneficial to both scholars and management of colleges in the development of competitive advantage and appropriate promotional strategies for college and academic programmes that appeal favourably to potential students than competitors in Ghana and other developing countries. The paper contributes to the literature in the area of access and management of higher education.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".