Quality Assurance in Asian Open and Distance Learning: Policies and Implementation
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
Open universities have emerged as an innovative pillar in the expansion of access to higher education participation, with single-mode distance education providers broadening access in many countries through economies of scale supported by large enrolments. These models raise questions about the quality of education provided. This paper reports on a comparative case study of quality assurance (QA) programs in distance education at three open universities in Southeast Asia. Focusing on QA development and implementation in learner support services, the study explored QA policies, supporting management practices and structures, and the influence of internal and external environmental factors, as identified through thematic analysis of data from semi-structured interviews and policy documents. The results showed many similarities in QA for learner support at the three institutions. Their learner support services were determined to be responsive to government and external quality agencies, external cultural and language factors, and student feedback.Editorial Note: Anak Bangsa Open University (ABOU) is a pseudonym used for another university, and there is no ABOU as such. All references are to actual documents and processes of the said university, but readers may not find the documents cited under ABOU in the references.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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