Collaboration for global e-learning impact
Bibliographic record
Abstract
The UK eUniversities Project (UKeU) was a major initiative designed to increase the presence of UK higher education in the global e-learning marketplace. The model was one of partnership - between public sector Higher Education Institutions (HEIs) and the commercially-driven UKeU, and between collaborating HEIs. It was judged unsuccessful by the Higher Education Funding Council for England (HEFCE) who wound up the venture in 2004. This symposium will ask the question "Where now for global e-learning?" The panel members will draw both on practical experiences of working on UKeU projects and on the work undertaken by the e-Learning Research Centre which is a joint venture between the Higher Education Academy and the Universities of Manchester and Southampton. The outcomes from three studies will be presented, each examining a different aspect of UKeU. 1) eLearning in UKeU: Through questionnaires and in-depth interview eLRC has examined the approach taken by UKeU to e-learning and the extent to which the model advocated was taken up and applied by the course development teams in HEIs. Is the activity-based learning object model developed by UKeU worth adopting? What other models make sense for global HE courses? 2) Managing risks: One of the impediments to e-learning is the perception of the different nature of risk compared to conventional teaching and learner support. UKeU recognised this and implemented a risk register that was revised and reviewed every quarter. Examination of some of the UKeU risk registers by the eLRC has revealed the scope of risks and their severity and impact as identified by UKeU. The contribution to overall risk levels from the business model, the marketing operation and the technology adopted will all be examined. Are there lessons to be learnt from UKeU for others involved in high risk activities? What can be done to manage, and where possible, eliminate risks of the sort encountered by UKeU? 3) Modelling UKeU: The eLRC is developing a UML model of the UKeU end to end business process. It is anticipated that this, and similar models, will be applicable to e-learning in other organisations. How helpful are models such as this in managing the end to end process of e-learning effectively? Is it realistic to try to industrialise the development process and the delivery of courses? How ready are HEIs to take this road? What are the human issues around such approaches that might impede their effectiveness? HEFCE's e-learning strategy now favours "blended learning" over "pure" e-learning. Does this mean that supporting students via e-learning to take courses entirely remotely should not be attempted? The symposium will use the evidence from the UKeU studies to inform the debate of what constitutes good practice in global e-learning. It will explore just how relevant e-learning could be to addressing the global deficit for higher education and examine the extent to which the perception of international students as cash cows runs counter to universities' belief in education as a tool for promoting equity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.838 | 0.623 |
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".