Model of the Independent Learning Campus Internal Quality Assurance System Program based on Artificial Intelligence
Bibliographic record
Abstract
Research on models of internal quality assurance systems in tertiary institutions online and digitally based on artificial intelligence in supporting the Merdeka Learning Campus Merdeka program in accordance with the cycle of determination, implementation, evaluation, control and improvement. The system is supported by an artificial intelligence approach to determine the implementation and achievement of the Merdeka Learning Campus Merdeka standard and to help universities detect early the impact of the implementation of Merdeka Learning Kampus Merdeka on the development of student competence. The implementation of the Merdeka Learning Campus Merdeka program is recorded in a database with cycles of Determination, Implementation, Evaluation, Control and Improvement to analyze compliance with the establishment, implementation, evaluation, control and improvement of standards for one cycle each year. At the evaluation stage, standard achievement will be produced whether it exceeds, is achieved or deviates to be followed up at the control and improvement stage. With this application, it helps tertiary institutions carry out the standards for the Merdeka Learning Campus Merdeka program standards to be carried out and developed according to the cycle of Determination, Implementation, Evaluation, Control and Improvement
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.
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 | 0.003 |
| 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 | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".