Perception on Service Quality in a Military Training Course Measured by the HEdPERF Scale and Its Relationship with the Students’ Characteristics
Bibliographic record
Abstract
Considering the educational services, the Brazilian Navy education aims to comply with continuous and progressive process of education, with own characteristics, which are constantly updated and improved based on some principles, such as guarantee of quality standard, continuous and progressive professionalisation, and integral and continuous evaluation. Therefore, this study aimed to assess the service quality of the training course at the Santa Catarina School of Sailor Apprentices, according to the students’ perception. For this evaluation, the HEdPERF scale was used. Data collection was performed by using printed questionnaires and the sample consisted of 178 students enrolled in the sailor training course who voluntarily participated in the study. The instrument for data collection was validated by confirmatory component analysis by using the SmartPLS 3 software. Descriptive statistics and regression analysis were performed to assess the data with SPSS software. As a result, the study revealed that the respondents perceived the dimension “academic aspects” as having the highest quality, whereas the dimension “non-academic aspects” was perceived as having the lowest quality. Regression analysis showed that variables such as total monthly family income, education level and main motivation to enter the armed forces have a statistically significant relationship with certain items of the HEdPERF scale, which was adapted to the present study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".