3D IPEA Model to Improving the Service Quality of Boarding School
Bibliographic record
Abstract
<p>he service quality is an important factor which affecting student performance, expectation and satisfaction in a boarding school. The traditional of Importance-performance analysis used to evaluate strength and weaknesses of a service quality factors. The models of Importance-Performance-GAP Analysis (IPGA) have developed by integrating the strengths of the importance and performance analysis (IPA) and the GAP analysis (Lin, et al. 2009). This study develops a 3D (three dimensions) service quality and gap model by extending the IPGA model through adding student expectations attribute. This method shows the useful of the IPEA (Importance-performance-expectation analysis) in 3D grid view and this method useful in evaluating service quality of school. This study identified 40 items and each item was rated using Likert scales that have a 5-point of levels. The results were obtained from 175 students from grade 7 to grade 12. The final result was divided in two different aspect; (1) management aspect and (2) building services and facility aspect. The IPA grid for management aspect shows that four items fall into fist quadrant (Keep up the good work), and seven items fall into the second quadrant (Concentrate here), two items fall into third quadrant (Low priority), and two items fall into forth quadrant (Possible overkill). The results of 3D IPEA are shown that two attribute putted in quadrant 3 and one attribute in quadrant 6. The findings of the study show that a management aspect and building facilities aspect are necessary to enhance the service quality of school. The results are useful to identifying real condition of building facility and help a boarding school to develop better service quality. </p>
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".