The Impact of Resource Input Model of Education Quality on the Overall Students' Perceived Service Quality/L'IMPACT DU MODÈLE D'ENTRÉE DE RESSOURCES DE LA QUALITÉ DE L'ÉDUCATION SUR LA QUALITÉ DE L'ENSEMBLE DES SERVICES PERÇUE PAR LES ÉTUDIANTS
Bibliographic record
Abstract
Students' retention and their academic performance are influenced by the service quality provided by higher education institutions (Sander, Stevenson, King and Coates, 2000). As such, it is vital to identify the determinants of the overall students' perceived service quality. The aim of this research is to evaluate and validate the determinants of the overall students' perceived service quality in a private higher education institution in Malaysia, based on the combination of both the 'inside-out' and 'outside-in' approaches as well as the resource input model of education quality. A total of 458 undergraduate business students from a private university in Malaysia participated in this research. The findings revealed that contact personnel, access to facilities, cost of courses offered, physical facilities of the tertiary institution and resource input model of education quality were positively related to the overall students' perceived service 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".