Open Educational Practices in Australia: A First-phase National Audit of Higher Education
Bibliographic record
Abstract
For fifteen years, Australian Higher Education has engaged with the openness agenda primarily through the lens of open-access research. Open educational practice (OEP), by contrast, has not been explicitly supported by federal government initiatives, funding, or policy. This has led to an environment that is disconnected, with isolated examples of good practice that have not been transferred beyond local contexts.This paper represents first-phase research in identifying the current state of OEP in Australian Higher Education. A structured desktop audit of all Australian universities was conducted, based on a range of indicators and criteria established by a review of the literature. The audit collected evidence of engagement with OEP using publicly accessible information via institutional websites. The criteria investigated were strategies and policies, open educational resources (OER), infrastructure tools/platforms, professional development and support, collaboration/partnerships, and funding.Initial findings suggest that the experience of OEP across the sector is diverse, but the underlying infrastructure to support the creation, (re)use, and dissemination of resources is present. Many Australian universities have experimented with, and continue to refine, massive open online course (MOOC) offerings, and there is increasing evidence that institutions now employ specialist positions to support OEP, and MOOCs. Professional development and staff initiatives require further work to build staff capacity sector-wide.This paper provides a contemporary view of sector-wide OEP engagement in Australia—a macro-view that is not well-represented in open research to date. It identifies core areas of capacity that could be further leveraged by a national OEP initiative or by national policy on OEP.
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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 | 0.025 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".