Architecture Students’ Satisfaction in Iraqi Private Universities: TIU-S in Focus
Bibliographic record
Abstract
The importance of service quality is undeniable. For years, numerous marketing professionals have researched its direct and indirect impacts on customer satisfaction and loyalty. Academics have proposed empathy, responsiveness, assurance, reliability, and tangibles as the primary drivers of service quality, university-related physical items or resources (technologic apparatus, smartboards, air conditioners, garden facilities, sports facilities, computer laboratories, etc.). The capacity of a service provider to offer essential service or acceptable and trustworthy responses to a student's demands or questions is referred to as reliability. This research aimed to benchmark the service quality dimensions of the architectural engineering department compared to other departments at Tishk International University-Sulaimani, Iraq. A quantitative research method has been applied. To do this, we have used ServQual and asked those questions to more than 100 students from architectural engineering, civil engineering, and business management departments. The data were analysed, and the results were initially analysed through regression analysis, and the obtained standardised weights of the regression analysis have been used for benchmarking after being normalised. The results show that the architectural engineering department delivered the best service quality compared to civil engineering and the business management department.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.007 | 0.001 |
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".