Global Perspectives: The University of South Australia (UniSA) Case Study
Bibliographic record
Abstract
This case study describes current developments in the change processes that are underway at the University of South Australia (UniSA) as it develops from a dual mode institution to one that embraces flexibility in delivery of all of its courses and programs. Forces operating in the context of higher education are causing traditional institutions to become dual mode. Institutions that were already operating as dual mode are developing a proliferation of program delivery arrangements that move beyond hybridisation. A number of Australian institutions claim to provide flexibly delivered courses where student centred learning processes are facilitated through the use of information and communication technologies.
 
 Hybridisation and the move to flexible delivery in Australia has partly been driven by changes in the socio-economic context of higher education that have forced universities to compete for income to sustain their operating costs. Offshore teaching and the provision of onshore fee paying courses for international students have required changes to delivery processes for resource-based teaching and online technologies and these changes have washed through institutions. However, for some institutions the development of flexible delivery has been to achieve a wider range of educational purposes for all students. Such purposes are often described in terms of the shift in focus: for example, from teaching to learning; from elite to inclusive; from "producer" to "consumer;" from local perspective to international; from credentialing (four year degree) to life-long learning (40 year degree).
 
 This case study looks at the ways in which UniSA course, student, regulatory, logistical and technological systems or subsystems are changing, both in response to extra-institutional influences and in relation to institution-wide development of greater flexibility in teaching, learning and program delivery. This analysis highlights the ways in which these subsystems interact with each other and the critical importance of shared vision to coordinate changes on multiple fronts within the institution and to facilitate internalisation and ownership of such change by its staff.
 
 The case study highlights how the logistical assumptions and arrangements of online education are significantly different in kind from those of traditional face-to-face or distance education and argues that moves to online education therefore require pervasive change. At the same time, online education can provide an integrating framework for different forms of delivery, thereby achieving synergies and economies of scale.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".