Optimization of Higher Education Internal Quality Audits Based on Artificial Intelligence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Internal Quality Audit is an independent and documented systematic testing process to ensure that the implementation of activities in higher education is in accordance with the procedures and the results are in accordance with the standards to achieve the goals of the institution. Quality can be guaranteed by ensuring that each individual has the skills he needs to do the job properly. Quality orientation in development life in Indonesia is something that is very urgent, must be supported and developed in order to respond to the trend of global competition. There are significant differences in the accreditation and quality assurance system with the previous version, it is necessary to develop a strategy by building an artificial intelligence-based system. The method used is to build an online system by involving experts and assessors to develop concepts in accordance with the points of the 9 criteria accreditation forms, to build a digital quality audit form for matching and the level of conformity between the implementation of higher education standards and the standards set, the benefit is to help universities implement digital and intelligent based internal quality audits, know the tri dharma standards of higher education that must be improved, maintained and deviated
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it