Perceived quality in higher education: an empirical study
Bibliographic record
Abstract
Purpose – The purpose of this paper is to address two issues. First research goal is about analyzing differences in perceived quality in higher education (HE) between a private and a public university centre. Second, the research aims to analyze which are the key dimensions in perceived quality in HE from the students’ standpoint. Design/methodology/approach – An analysis based on a modified SERVQUAL instrument was used to respond to the objectives put forward. Then, a mean comparison and covariance structure analysis approach was carried out to test the differences in perceived quality between the students from both centres, as well as the dimensions with higher influence in perceived quality. Findings – The results suggest that tangibility and empathy dimensions are the most influent variables on perceived quality in HE. Furthermore, some relevant significant differences were found between the public and the private centre. Research limitations/implications – The results give an opportunity to HE institutions’ managers to develop enhancing quality strategies for their institutions, given that the present study relies on a sample of actual undergraduate university students. Originality/value – The present research provides with a comparative analysis between a private and a public centre, in order to assess which one offers higher educational and teaching quality from the students’ viewpoint, as well as an approach to the main variables in HE perceived quality.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".