The Practice of a Quality Assurance System in Open and Distance Learning: A case study at Universitas Terbuka Indonesia (The Indonesia Open University)
Bibliographic record
Abstract
Quality assurance for distance higher education is one of the main concerns among institutions and stakeholders today. This paper examines the experiences of Universitas Terbuka (UT), which has initiated and implemented an innovative strategy of quality assurance (QA) for continuous improvement. The credo of the UT quality assurance system is "We write what we do. We do what we write. We check. We improve continuously!" Implementing a quality management system at the UT, a mega-university with a student body of more than a quarter of a million and which involved a network of participating institutions and regional centres, was a formidable task to accomplish. To achieve its lofty goal, UT adopted and contextualised the draft of the Asian Association of Open Universities (AAOU) QA Framework to launch its own quality assurance program. This has taken a great deal of commitment and participation of all staff involved. QA at the UT required systematic and step-by-step processes, including development of the QA framework and job manuals, raising awareness and commitment amongst all staff involved, internal assessment, and integration of QA programs into the university's annual action plans, external assessment and benchmarking. This paper concludes that quality assurance must be developed as institutional policy and strategy for continuous improvement.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".