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Self-Organized Learning Networks for Lifelong Learning RTD Programme 2003-2008

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Title: Self-Organized Learning Networks for Lifelong Learning RTD Programme 2003-2008


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.lear
ningnetworks.org Hannover, November, 24th 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
  • RTD projects in several themes
  • Standardization activities
  • International Expert groups
  • 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
  • Network of interacting persons and resources
    heterogeneous lifelong learners, experts, tutors,
    learning resources and tools in some knowledge
    domain
  • Network of interacting distributed devices (e.g.
    computers, mobiles, )
  • Network of interacting providers for lifelong
    learning resources and services (institutions,
    libraries, publishers, associations, companies, )

8
Programme addresses two key issues
  • Establish the emergence of lifelong learning into
    a distributed, heterogeneous network of learners,
    providers and software agents
  • 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
  • Development and use of Activity Nodes How to
    design, create, share, use units of learning in
    the Learning Network
  • Learner Positioning in Learning Networks How to
    position new and existing learners in a Learning
    Network independent of curriculum or institution
  • 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)

15
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16
Content Packaging Learning Design
17
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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 modelsmodel 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/chapt
    ers

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
  • Building an abstract change model that provides
    entry points for the development of tools 
  • 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 individuals 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
29
Positioning in Learning Networks
Goals Destination a position in a learning
network which reflects the mastery of certain
competencies
  • 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)

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
  • Which information should be fed back (eg, success
    rate, time taken)
  • How? As abstract directed graphs? Landscapes of
    competencies?
  • When? Always show everything?

Learner/activity interaction data
Micro-level interactions
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?

36
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37
ALFANET Active Learning For Adaptive Internet
Peter van Rosmalen Open University of the
Netherlands
November,2003
38
Project Aims
Alfanet aims to develop new methods and services
for active and adaptive e-learning. The projects
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
IMS LD
Authoring tool design time
Audit feedback to the design based on runtime
monitoring
Unit of Learning IMS-LD
properties
IMS-LD-engine
  • Agents
  • Audit
  • Adaptation
  • MAPM
  • Tutor
  • Designed role
  • Observator role

Adaptation based on the design
Adaptation based on runtime monitoring of agents
and tutors
Presentation layer
43
Technical Architecture
LD



En

J2EE Application Server
Server




Security Layer
Presentation Layer
Tracker
Dispatcher



1n


Services


Object Model
Common


Repositories
Data



Authoring Tool
44
Current Status
45
Current Status
  • 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
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