Investigation to Standardise the Toolkit for Assessing Training Programs Quality in Viet Nam under the Effects of Quality Accreditation
Bibliographic record
Abstract
This study has collected the data, analysed it, and drawn the necessary scientific conclusions to standardise the toolkit to evaluate educational accreditation activities' influence on training program development in Vietnam. The research method of the article includes building survey questionnaires and collecting data from 80 lecturers in 10 universities in Vietnam and then using secondary inferences based on data analysis using the Statistical Package for the Social Sciences version 26.0 and Analysis of Moment Structures software. The results obtained include the design and standardisation of the closed-question questionnaire according to 5 levels of the Likert scale. Based on the criteria of the questionnaire, the research showed that the criteria were correctly arranged, consistent with the internal content of the factors in the independent and dependent variables, as well as with the proposed research model. From that, the study has provided a solution to standardise a usable accreditation toolkit suitable for higher-education conditions in Vietnam.
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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 | 0.037 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".