Quality Assurance for Postgraduate Programs: Design of a Model Applied on a University in Chile
Bibliographic record
Abstract
The quality of Education in Chile is a controversial topic that has been in the public debate in the last several years. To ensure quality in graduate programs, accreditation is compulsory. The current article presents a model to improve the process of self-regulation. The main objective was to design a Model of Quality Assurance for Postgraduate Programs in order to constitute a theoretical, mathematical, and informatics reference that would optimize the processes of self-regulation, self-evaluation, and accreditation of master and doctorate programs from the Universidad Católica de la Santísima Concepción, Chile. This descriptive research is based on a mixed methods approach. The proposal was intended through theoretical and empirical references related to the accreditation systems. The analysis process was conducted with key informants, and the informatics instrument was created and validated through expert judgment. After the analysis, the model was optimized considering the expert’s suggestions. As a result of the optimization process, a matrix of eight dimensions was obtained and it is available online in order to be used by the heads of postgraduate programs. Finally, a model with four main stages was achieved in order to install a self-regulation and a self-evaluated culture that leads to accreditation as evidence of the quality of postgraduate programs.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".