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Record W22495962 · doi:10.1055/s-0031-1297966

Collaboration for global e-learning impact

2005· article· en· W22495962 on OpenAlexaboutno aff
Jonathan Darby, Maarten de Laat, P. L. Wilcox, Elizabeth P. Roberts

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsHigher educationGeneral partnershipQuarter (Canadian coin)Work (physics)Scope (computer science)Public relationsPanel discussionJoint ventureMedical educationPolitical sciencePedagogyBusinessSociologyEngineeringComputer scienceMedicineFinanceGeographyBusiness administration

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score0.231

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0040.004
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.8380.623

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.

Opus teacher head0.014
GPT teacher head0.330
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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