Collaboration for global e-learning impact
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,838 | 0,623 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».