Evaluation of Graduating Students of the Impact of Graduate School Education and Services in Rizal Technological University
Bibliographic record
Abstract
The students are the reasons for the establishment of learning institutions which are the key factors to quality education. At present, the mission of the RTU-GS is to produce world-class professionals and leaders to meet the multifarious expectations and needs of society. Since 1977 up to presents, no one has ever conducted a research on graduate students‘ assessment of the Graduate School and its student services. It is in this end that the researchers brought about an exit interview to Graduate School graduating students.This study used the causal comparative design and had 80 respondents representing the RTU graduating students‘ population for school year 2012-2013. The respondents were described as to gender, age, GS course and number of year of degree completion.From the salient findings of the study, the researchers concluded that the GS graduating students are very satisfied with the personal impact of education, the development of their professional skill, and the personnel and various offices. The GS students suggest the fixed format on thesis writing, special lanes or windows for business transaction and the maintenance of good interpersonal relationship between school personnel and students. The overall rating of the GS students of the education is very satisfactory. Finally, there are significant differences in the assessment of GS students by personnel and offices.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".