Learning from e-Learning: Initial experiences from the European Learning Grid

1. Learning from e-Learning:Initial experiences from the European Learning Grid Colin Allison, Stuart Purdie, Tim Storer Computer Science University of St Andrews

2. ELeGI Project • European Learning Grid Infrastructure is a 4 year EC FP6 funded “Integrated Project” which started in February 2004 • 23 partners • 12 universities • 2 open universities (OU and HOU) • 4 research institutes • 4 commercial technology companies

3. ELeGI: Motivation • dominance of the “information transfer” learning model (assumed and supported by most e-learning products) • content management • easily achieved through simple use of the Web • lecturer’s job is to select, or sometimes produce, content • little scope for exploratory, interactive or collaborative learning • wish to foster “knowledge construction”, i.e. • “collaborative, experiential, personalised, realistic, contextualised and ubiquitous learning modes “

4. Technologies and Standards Learning and Collaboration Models Service Elicitation and Exploitation Scenarios (SEES) Analysis and Synthesis Software Architecture Design Prototype Infrastructure Implementation Feedback Deployments and Evaluations ELeGI Approach

5. Content sharing Presentation mark-up Product/download Producer/consumer bolt-on security Ad hoc identity management (cookies) Secure transactions (awkward..don’t press that button…) Authentication – ad hoc, no cross domain Resource sharing Semantic mark-up Dynamic service Virtual communities detailed security model PKI Certificate based identity management Cross domain trust models Secure transactions Why Grid? Grid Web

6. two aspects to the Grid • Original Grid: “the anatomy of the Grid” -> • distributed supercomputing i.e. the resources to be shared are high performance processing, networking and storage facilities • For the former to be effective the “physiology of the Grid” had to evolve: OGSA: Open Grid Services Architecture • secure resource sharing across multiple autonomous domains • Enabling collabration across domains • “Virtual communities” • Can OGSA be used as an open framework for e-learning?

7. Prototypes: re-engineered learning tools/facilities/environments • Purpose: to understand the meaning of being “OGSA conformant”, and issues in implementing Grid learning services, without worrying about pedagogical goals and models • Finesse -> Finesse Grid Services • BuddySpace -> BuddySpace Grid Interface • GRASP (OGSI.Net) -> GRASP (WSRF.Net) • IWT -> IWT “Grid Aware” • VCLab -> VCLab Grid Services

8. Learning Models • General • Socio-constructivist, knowledge construction • Real world , Collaborative, Experiential, Context aware • Formal Learning model “D18” • Focused entirely on Virtual Scientific Experiments • Maps onto the IMS-Learning Design format • Output is a sequence that feeds a virtual control laboratory • “personalised” • Informal Learning “D20” • Social learning; learning as a side effect; collaborative environments, “enhanced presence”

9. Standards and Specifications • Necessary for interoperability: W3C, IETF, GGF, IEEE, IMS, etc. • OASIS (Organization for the Advancement of Structured Information Standards) • Web Services Resource Framework (WSRF) • Universal Description, Discovery and Integration (UDDI) protocol • Web Services Description Language (WSDL) • Security: X.509 certificates, https, .. • Globus Toolkit 4 (GT4) – open source, public domain, multi-platform • WSRF.NET – Microsoft Windows only

10. SEES#2 EnCOrE • Main purpose: to build an informal learning service based on a virtual scientific community • Collaborative knowledge capitalization • Development of a dictionary of organic chemistry as a component of an electronic encyclopaedia: EnCOrE • Domain: synthetic organic chemistry • Informal learning: • Of the users (students, researchers, engineers) • Of the authors about their own language and about concepts and techniques required to build such a service • Formal learning: • Training new participants to the EnCOrE activity

11. SEES#3a eQualification • Scope: Formal and Informal Training • Testbed = ASIMIL (Aeronautical Simulator) • Training Session • Briefing • Simulation • Debriefing • The e-Qualification process • Process allowing to qualify and certify formal and informal learning • Adapted to the training domain • Objective = accreditation • e-Qualification process is efficient • and results are matching with the training norms

12. SEES#4 Masters in ICT • Purpose of the SEES • Formal learning • Remote institutions collaboration to offer same program • Virtual Campus • Common courses among remote student communities • Virtual Classrooms • Common scientific experiments • Virtual Scientific Experiments • Target group • AIT students, CMU students, Japan CyLab students

13. SEES#5: Masters in Physics at HOU • Purpose of the SEES • Formal learning • Collaborative/Social Learning in Physics Course at HOU • Target group • HoU Students • Main characteristics • Students cooperate, perform experiments • Knowledge construction through the exchange of data and knowledge • Types of services • Virtual Experiments • Virtual Communities support

14. ELeGI architecture – but what‘s really needed?

15. Core architecture

16. Current Demonstrators • VSE demo: • Using Reload to create an IMS-LD document • Using IWT-GA and CopperCore to control VCLab according to the IMS-LD script • Generic C&C and enhanced presence: • BuddySpace (deployed) • Grid Shared Desktop (deployed) • Interoperability of Grid services(deployed) • Using Finesse Grid Services (FIGS) and Buddyspace Grid Service Interface (BUGSI) to create a flexible learning environment with enhanced presence for: • Business finance, Financial modelling • Decision making in team management scenarios • SensaSim Grid Service + IMS-LD(piloted)

17. Evaluation Framework • A deployment is a set of ELeGI learning events for an identified group of users for a specific period • Deployment and Evaluation Templates • each SEES is evaluated by multiple deployments, each with a separate case file • each case file is organised into inputs and outputs • Inputs include the D&E document, evaluation materials, such as questionnaires, and results including completed questionnaires and system logs • Outputs include analyses and summary reports

18. Case File Outline

19. Quality of Service Issues for the Grid: WSRF and GT Dangerous assumption – that there will be QoS “on demand” for the Grid • Realistic assumption: Grid-based learning environments must make use of available infrastructure e.g. the Internet

20. Browser client Web server (Tomcat) Grid server container GT4 Java Core Database e.g. mySQL Simple small packet (~ 100 bytes content) http(s) tcp jdbc-mySQL tcp 112ms SOAP http(s) 127ms From before tcp handshake until just before rendering 100ms 22ms 109ms 20ms jdbc-mySQL tcp SOAP http(s) http(s) tcp

21. Conclusions • Grid offers a secure resource sharing model • Resources are virtualised as services • Resources/services are described using semantics for description and discovery • Web is still important for delivery of services and user interfaces – but multiple interfaces now possible • Decoupling of functionality from UI has shown good results • Standards and specifications helps towards interoperability • Project has demonstrated interoperability using Grid services • Not clear yet if the current OASIS standards give the all the flexibility and interoperability needed for e-learning framework • Currently working on UDDI and Grid Shib

22. References • Allison, C., S.A. Cerri, A. Gaeta, M. Gaeta, and P. Ritrovato, Services, Semantics and Standards: Elements of a Learning Grid Infrastructure. Applied Artificial Intelligence, 2005. 19(9-10): p. 861-879. • Ritrovato, P., C. Allison, S.A. Cerri, M. Gaeta, S. Salerno, and T. Dimitrakos, eds. Towards the Learning Grid, Advances in Human Learning Services. Frontiers in Artificial Intelligence and Applications, ed. J. Breuker, et al. Vol. 127. 2005, IOS: Amsterdam. 239. • www.elegi.org

23. Thank you! • Questions?