1. Self-Organized Learning Networks for Lifelong Learning RTD Programme 2003-2008 Rob Koper, Peter Sloep, Colin Tattersall, Peter van Rosmalen Educational Technology Expertise Centre Open University of the Netherlands www.learningnetworks.org Hannover, November, 24 th 2003

2. Overview Introduction to the Programme Rob Koper Semantic Representation of Nodes (IMS LD) Rob Koper Agent technologies to support teaching functions in learning networks Peter Sloep Navigation in Learning Networks Colin Tattersall AlfaNet (use of LD in a context of agent technologies and collaborative learning) Peter van Rosmalen

3. Introduction Open University of the Netherlands ­Started in 1984; National (Public) Institute ­Two missions: 1. provide open distance education and 2. innovate education (in general)

4. Open distance education ­6 faculties, 23000 students (age avg 32), ­9 bachelor/master programmes; students can make a free selection of courses during their life ­20 study centres in Netherlands and Belgium ­Develop self study materials in multidisciplinary teams ­Deliver education through a variety of technologies (print, cd-rom/DVD, telephone, internet, face to face contact sessions, practicals, etc.)

5. Innovation Educational Technology Expertise Center ­RTD programme into Learning Technologies ­RTD programmes into LT 1998-2002: Educational Modeling (EML, IMS LD, Edubox) 2003-2008: Learning Networks for LifeLong Learning ­Positioning: a. Major expertise ‘educational technology’ b. Major focus is: innovation through development of new learning technologies c. Between Educational Science and ICT technologies d. Bring in educational requirements that are specific enough to be implemented in ICT environments

6. Learning Networks Programme 2003-2008 Basic activities 1.RTD projects in several themes 2.Standardization activities 3.International Expert groups 4.EU projects and national RTD projects

7. Objective of Programme Develop a coherent set of learning technology models, specifications & tools to establish a new effective, efficient, attractive and accessible approach for higher, distributed lifelong learning, called learning networks. Network in the interpretation of: 1.Network of interacting persons and resources: heterogeneous lifelong learners, experts, tutors, learning resources and tools in some knowledge domain 2.Network of interacting distributed devices (e.g. computers, mobiles, …) 3.Network of interacting providers for lifelong learning resources and services (institutions, libraries, publishers, associations, companies, …)

8. Programme addresses two key issues 1.Establish the emergence of lifelong learning into a distributed, heterogeneous network of learners, providers and software agents 2.Help staff members to do their work more effective and efficient (minimize staff work load)

9. Issue 1: Lifelong Learning Some general questions: ­How do lifelong learners learn? What do we know of the learning behaviours and preferences of persons during their lifetime and career ­How can we support lifelong learners with new learning technologies?

10. Issue 2: Efficiency of Support Basic Question. How can we: ­make learners more productive, responsible, adapt to prior knowledge, provide freedom of navigation (learner), ­produce high quality learning resources (knowledge) ­provide more formative feedback on the productions (assessment) ­can involve more experts and practitioners, handle heterogeneity in groups (community) ­… without increasing (or better: decreasing) the workload for the staff members involved.

11. Main instruments in programme ­Models, principles and rules to establish self-organized, distributed lifelong learning ­agent technologies (in context of semantic web) to support the actors in the learning process (learners, tutors/experts, developers) and ­interoperability specifications and standards (e.g. for portable learner dossiers, competencies, architectures, etc.)

12. Main programme themes 1.Development and use of Activity Nodes How to design, create, share, use units of learning in the Learning Network 2.Learner Positioning in Learning Networks How to position new and existing learners in a Learning Network independent of curriculum or institution 3.Navigation in Learning Networks How to navigate in Learning Networks, using & exchanging recorded learning tracks, learning routes and learning patterns in Learning Networks

13. IMS Learning Design In Short ­New standard from IMS (februari 2003) www.imsglobal.org ­Based on our previous work on EML (Educational Modelling Language; published december 2000) ­Objective is to model complete Units of Learning that can be transferred to different systems and contain the compete description of its designed content and process. ­Provides an integrated framework for different other IMS specifications (incl. LOM, QTI, LIP, CP, RCD, SS)

14. What is a Learning Design? ­The learning design specifies the specific workflow and content in the learning process: which role has to performs which activities, using which resources and services in which order in order to attain the learning objectives in the best way, taking care of individual differences (LD is an instance of a pedagogical model: a concrete application of a pedagogical model for a specific target group, for specific learning objectives and a specific domain)

16. Content Packaging & Learning Design

18. Why IMS LD? ­Pedagogical meta model ­Offers a level of abstraction enabling different educational models to be described, including: Learner, Knowledge, Assessment, Community Centered Approaches (in the different ‘schools’) ­Software which knows about the meta-model can interpret specific models—model an approach to learning (eg problem based learning) and have it executed (‘played’) ­Complete specification of a course (not only the resource part; needed for automation and interoperability) ­Moves the focus from Learning Objects to Learning Activities

19. Some References ­IMS LD (download www.imsglobal.org) ­www.learningnetworks.org (EML) ­See: list with recent journal articles/books/chapters

20. Agents for Support Activities (ASA) Peter van Rosmalen, Peter Sloep November, 2003

21. Rationale for ASA 1.Support staff lends support to many different kinds of Learning Activities. 2.This puts quite a strain on the support staff. 3.From an institutional point of view this means that providing support for learners rapidly becomes unaffordable.

22. Premises ­ Establish learning related interactions between distributed actors and distributed resources in a Learning Network. ­ Do so efficiently: minimally maintaining the intensity and learning quality of the interactions without increasing staff workload.

23. Objective To develop learning technologies (agents) that help tutors support their students in learning networks by 1.Building an abstract change model that provides entry points for the development of tools 2.Developing functional prototypes of these tools and test them in pilots.

24. Outcomes a model of how tutors will be supported in their support activities for the Activity Nodes in a Learning Network prototypical software modules that qualify as generic support agents for tutors a model of how agents operate within the context of a design specified in IMS-LD.

25. Some details ­Focus on the tutor: support the tutor, not the learners directly ­Focus on agents that will build upon language technologies (e.g. support for e-mail answering and essay grading)

26. Navigation in Learning Networks Colin Tattersall

27. Navigation in learning networks ­Exploiting collective learner interactions to help learners select paths through learning networks towards their educational goals. ­“Others who went before you proceeded that way to reach their educational goals”. ­A feedback loop which guides learners in deciding what to do next. ­The idea is that an individual’s chances of reaching his or her goals are improved through insights on how others have successfully reached their goals. ­Aim: to improve educational yield using principles of self- organisation

28. Educational yield m learners n learners Time period t t0t1 Educational yield is the percentage of learners which successfully meet certain criteria in a given timeframe. Success might be ‘accumulating study points’, undergoing an oral examination, etc Yield = ((n/m) * 100)

29. Positioning in Learning Networks ­the point of departure (which competencies an individual already possesses) ­destination (which competencies are desired to be gained) ­the assessment of whether the destination has actually been reached (i.e. testing whether competencies have been mastered) Goals = “Destination” = a position in a learning network which reflects the mastery of certain competencies;

30. Navigation vs Positioning ­Positioning: ­“I have these competencies and I want those” ­“I am here and I want to be there” ­Navigation: how to get from here to there ­(Must carry out all units of learning in a certain order) ­Must carry out all units of learning but can vary order ­Travelling Learner Problem ­Can select which units of learning to perform vs. which to skip but must follow a particular order ­Can select which units of learning to perform and in which order

31. Emergent, macro-level information Track1: AN1, AN4, AN6: learner1, learner6, …; Track2: AN1, AN6: learner4, learner9,…; Track3: AN1, AN10, AN2, AN4: learner7; Track4: AN1, AN2: learner99, learner77, … Learner/activity interaction data Micro-level interactions Which information should be fed back (eg, success rate, time taken) How? As abstract directed graphs? Landscapes of competencies? When? Always show everything? Feedback in self-organising learning networks Feedback to learnersFeedback to providers Presentation of collective learner behaviour Filters

32. Interaction data

33. Inspiration: self-organisation by ants

34. Learning Tracks & Roadmap Tracks are left behind by learners like the pheromones left behind by ants The intensity of the track reflects chances of success, number of attempts, time taken, … ?

35. Feedback to help answering … ­Learners ­How did other learners progress in this learning network from where am I now? ­Which path through the learning network offer the most chance of success? ­What has been the fastest path taken by others through this Learning Network? ­Providers ­What percentage of learners followed the learning route(s) prescribed in the curriculum through the learning network? ­Is the learning route the most efficient way to progress through the learning network or are learners identifying better paths? ­Where are learners slowing down or dropping out?

37. November,2003 ALFANET Active Learning For Adaptive Internet Peter van Rosmalen Open University of the Netherlands

38. Project Aims Alfanet aims to develop new methods and services for active and adaptive e-learning. The project’s target is to deliver a tested set of components for e-learning providers that will provide significantly enhanced individual learning, through technologies with adaptive features and approaches. Key issues: Adaptation – individual needs design- & runtime: Links, contents & collaboration Feedback loop for the design Agent-supported architecture Standards: IMS-LD, …… Partners: SAGE, UNED, EDP, KLETT, ACE-BNET, OUNL

39. Core Components and Standards OpenACS - (UNED) provides facilities for collaborative learning. IMS-LD - to enable advanced pedagogical designs including adaptation - to enable communication between the different actors – designers, tutors & agents IMS-LD authoring tool - (ACE-BNET) IMS-LD engine - (OUNL)

40. Agents Learning Adaptation Model (UNED) - will support the learner in collaborative learning, navigation and content selection. Audit (OUNL) - will support the design team with feedback concerning the initial design and the actual use/results Multi-Agent Pedagogical Model (OUNL) -will support the design team with the selection & use of LD-models. - will support the learner during the execution of selected activities.

41. IMS LD

42. properties IMS-LD-engine Agents: Audit Adaptation MAPM Tutor: Designed role Observator role Authoring tool: design time Presentation layer Adaptation based on the design Adaptation based on runtime monitoring of agents and tutors Audit feedback to the design based on runtime monitoring Unit of Learning IMS-LD

43. Technical Architecture LD En J2EE Application Server Security Layer Presentation Layer Dispatcher Tracker Server Services Data Common Repositories Authoring Tool Object Model 1…n

44. Current Status

45. Alfanet version 1 (1 January-2004) Integrated: - IMS LD Authoring Tool - OpenACS (collaborative framework) - IMS LD level A engine Partly integrated / partly demonstrators - Learning Adaptation - Audit - MAPM Evaluation round 1 (January-March 2004) - design and learner evaluation of two courses