OPEN-INTELLIGENT-EFFECTIVE Barbara Kieslinger kieslinger@zsi.at Centre for Social Innovation

1. OPEN-INTELLIGENT-EFFECTIVE Barbara Kieslinger kieslinger@zsi.at Centre for Social Innovation

2. Corporate Learning Management....state of the art Publishing House Magazins,Journals Today‘sKnowledge Worker OnlineBookstore Books OnlineMarketplace Courses Tool:Web Browser Intranet Tutorials Intranet Best PracticeStudies OPEN

3. Current drawbacks • Lack of transparency of knowledge offerings • Increased search costs • Personalization is site-based (e.g. everything you buy at Amazon.com) vs. process-based personali-zation (e.g. gain know-how on “Web Services”) • No decision support OPEN

4. Corporate Learning Management...future Publishing House Magazins,Journals Tommorrow‘sKnowledge Worker OnlineBookstore OPEN Books OnlineMarketplace Smart Space for Learning PLA Courses Tool:Web Browser+ PersonalLearning Assistant (PLA) Intranet Tutorials Intranet Best PracticeStudies

5. RequirementsSystem interface framework NEW Learning Resource Management Access Control Access & Delivery Provision Querying APPLICATION LAYER PENDING SystemRegistration UserAuthentication ServiceAnnouncement Inspection ADMINISTRATION LAYER OPEN

6. First Prototype OPEN

7. Issues • Broker[UBP]-centred approach less flexible  administrative and performance bottleneck • Adoption of common schema by provider increases implementation costs  local mapping instead of enforced schema • Lack of interoperability on schema level  mapping of search results required • High implementation effort for plugging in new node sound interface definition required • Performance of Peer-to-Peer Network Identification of performance bottlenecks OPEN

8. Requirements for a query interface • The query interface must be designed to query heterogeneous metadata schemas. • The query interface must not require a specific query language. • The query interface must build upon existing standards of the W3C such as XML, RDF, and SOAP. • The query interface must not require a specific network architecture. • The query interface must be based on a light-weight design, which allows for a fast implementation. • First public draft:http://nm.wu-wien.ac.at/e-learning/interoperability/query.pdf • Submission to CEN/ISSS Standardization Workshop(Work Item on Interoperability of Repositories for Learningby Erik Duval, Simos Retalis, and Bernd Simon) OPEN

9. Next Milestone Provision Interface Query Interface Amazon Interface UBP-basedPersonalLearningAssistant Amazon Clix Arel Edutella P2P Network IteachYou UBP-basedMarket-place Clix OPEN

10. What is a Smart Space? • A Smart Space for Learning is a system, which aims to manage the distribution and consumption of Learning Services via a Personal Learning Assistant. • “Space” depicts a network of educational nodes, designed for the provision of a heterogeneous set of learning services. “Smart” refers to the use AI techniques (e.g. reasoning) and shall ensure an optimized selection of learning services. INTELLIGENT

11. Who is smart? • PLA, the personal learning assistant, performs the search for suitable learning services based on the learner's individual profile, processes the selected services and supports the evaluation of the learner. • Learners with different background • Learners with different goals, preferences, aims • Learners with different learning styles INTELLIGENT

12. The Situation Distributed content/services Distributed standard based metadata descriptions about: Content/Services Relationships between the content/services Learner Logic Programs Query Add restrictions to queries Enhance metadata Content/Service Relationships Content/Service Metadata Logic Programs Learner Model P2P INTELLIGENT

13. Summary of Personalisation Approach of ELENA • Service/Content metadata seen as some constraints on use for learning services (LS) • Learning service metadata are retrieved according to matching between learner profile and LS metadata • Rules determine how adaptation is performed based on the matching learner profile and LS metadata • Standards: LOM and Dublin Core INTELLIGENT

14. Learner Profiling • Why: To be able to customize (adapt) information to specific person • Different user characteristics can support personalization • Current State: • IEEE PAPI: • IEEE Public and Private Information (PAPI) for learner (IEEE P1484.2/D7, 2000-11-28) • IMS LIP: • IMS Learner Information Profile (v1.0, 2001-3-9) • Which way to go? (standard analysis, needs analysis) INTELLIGENT

15. Resulting Profile • ..... after comparison • Study performance • Performance, portfolio, certification • Identification • Calendar • HRP • Job, Title, Department • Other user features • Preference, Goal, Interest INTELLIGENT

16. PLA Search Interface INTELLIGENT

17. PLA Results Interface INTELLIGENT

18. What we need Smart Spaces for? • We want to assist in the process • of making training management • more effective for the • corporate world, by • introducing Smart Spaces for • Learning. EFFECTIVE

19. Interviews • Interview procedure -- 2nd • round: • 1.Introduction to the study • 2.Pre-questionnaire • 3.Successful / Failed Cases • 4.Scenario validations • combined with • questionnaire • 5.Claims analysis for • different artefacts and • features • 6.Wrap-up and conclusion EFFECTIVE

20. Findings 1/2 • Companies spend resources on life-long learning (1-1,5 % of EPIDA) • § Older, larger companies still have centralised behaviours, deciding employee training at boards • § Employees build perceived trust with customers by getting certified degrees • § Employees have annual meeting with HR manager. • § Companies assign their training budget on an employee-basis or on a department-basis. • § Personnel departments aim to maintain training accounts per employee in order to get the training partly refunded when the employee leaves the company within a specific time period. EFFECTIVE

21. Finding 2/2 • § Companies choose learning services from recognised companies/institutions • § Companies allow departmenthead/employees to choose learning services • § Employees some times take • 'wrong‘ decisions when it • comes to the selection of • learning services EFFECTIVE

22. Reflections on a Smart Space • A corporate demand for a well-managed central market place for learning services does exist. (Google for learning services). • § Well-managed means that providers are carefully selected, references to providers do exist, and the portfolio of the learning service offer exceeds a significant size. • § Learning service markets are local. Building a single European corporate portal for learning services does not make sense. Language and location are too big barriers. à Most effective filtering attributes: period when learning service is intended to be consumed, anguage and location • § E-Learning is hardly accepted by the corporations. Sometimes failed projects do exist. Hence, the learning service portfolio also needs to include presential courses. EFFECTIVE

23. User centred approach • Involve users e.g. companies with training management • in design • trials • and surveys EFFECTIVE Smart Space for Learning

24. Thank you! • www.elena-project.org