Modelling Quality Assurance System Process Using UML Notation
Bibliographic record
Abstract
Higher learning institutions (HLIs) employ academic quality assurance (AQA) approach to assess quality performance in the higher education system. It aims to assist in the process of obtaining accreditation and recognition for HLIs. Modelling the AQA process is significant for understanding the current process and subsequently developing an information system. Therefore, this paper describes steps in modelling the academic quality assurance (AQA) process through unified modelling language (UML) notation. Metamodelling approach is used to identify the domain concepts and relationships before transform into UML-based foundations. The findings propose AQA domain into three different UML diagram which are use case modelling, structural modelling and behavioural modelling. Use case diagrams show the interaction between users and the system. While class diagrams structure the AQA endeavour process into categorisation systematically. The communication diagrams show the behavioural structure of the system through messages that pass between the objects in the interaction. This work gives stakeholder’s insight into AQA endeavour through UML notation proposed; i.e.; understanding the AQA process, assist in decision making process and helps in designing new system related to the endeavour process.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".