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Transcript of 1 WP1 Personalized Adaptive Learning. Overview Introduction D1.10 A SECI-based framework for...
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WP1 Personalized Adaptive Learning
Overview
• Introduction• D1.10 A SECI-based framework for
learning processes @ work • D1.11 Integration of adaptive learning
processes with IMS Learning Design considering corporate requirements
• D1.13 Current and future perspectives for Personalized Adaptive Learning
• Summary
3
Introduction
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WP1 Deliverables• D1.4/D1.6 User interface requirements and solutions in corporate e-Learning /
Specification of requirements and the state of the art in personalized adaptive learning especially regarding corporate e-Learning (Report)
• D1.1 Requirements and solutions for personalized adaptive learning and systematic description of personalized assessment tools (Project Grapple)
• D1.3 Learner models for web-based personalised adaptive learning: current solutions and open issues
• D1.7 Web portal for professional education
• D1.2 Interoperability of adaptive learning components (ET&S journal paper)
• D1.5 Privacy and data protection in corporate e-Learning
• D1.8 Specification and prototyping of personalized workplace learning (IJLT paper)
• D1.9 Interfacing adaptive solutions with corporate training systems (JIME paper)
• D1.10 A SECI-based framework for learning processes @ work
• D1.11 Integration of adaptive learning processes with IMS Learning Design considering corporate requirements (Online Showcase)
• D1.13 Current and Future Perspectives for Personalized Adaptive Learning
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Deliverable 1.10 SECI-based Professional Learning Process Framework
Knowledge
Explicit
Tacit
Combination
Socialization
Inte
rnal
iza
tio
n Ex
terna
lizatio
n
The SECI spiral of knowledge creation
Indiv.
Group
Organization
Knowledge
Explicit
Tacit
Combination
Socialization
Inte
rnal
izat
ion E
xternalizatio
n Dialoguing ba
Exercising ba
INPUTnew
individualunderstanding
Systemizing ba
Originating ba
Community building
tools
Discussion supporting
tools
Conceptual modeling
tools
Reflective analysis
toolsR
efl
ec
tin
gE
mb
od
yin
gConnectingDeducing
ExperiencingEmpathizing
Artic
ula
ting
Co
nc
ep
tua
lizing
OUTPUTnew
collectiveunderstanding
INPUTnew
collectiveunderstanding
OUTPUTincreasedcollective
understanding
OUTPUTnew
individualunderstanding
INPUTincreasedcollective
understanding
OUTPUTincreasedindividual
understanding
Indiv.
Group
Organization
INPUTvisions
challengesactivities
Intra Intra
SECI-based Communication Process
I E I EConscious
Subconscious
Explicit
Tacit
S
C C
S
Formal
Informal
Inter
I E
C
S
I
E
C
SI
E
C
S
C
S
Notational Simplification
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Some screenshotsfrom the Conzilla model
Layers of the PLPF
The generic PLPF
Adding the Business Roles layer
The generic PLPF with Business Roles
Documenting Ambjörn’s learning about the project PROLEARN as related to the Aims, Controls, Input, Output, Support and Effects during the project at the formal and informal level
A Learning Frame for Ambjörn in PROLEARN with Questions, Possible answers (“Theories”), Tests and Reflections
Pointing to the frame brings up information about it
Pointing to the corner icons brings up information about them
Clicking on the frame brings out an icon of the underlying mapDouble-clicking on the frame opens this map
Generic PLPF - different perspective
Individuals with Business Roles
in Projects
Teams in Projects
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D1.11 Integration of adaptive learning processes with IMS LD considering corporate requirements
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Taxonomy of Adaptive Methods
What is adapted …
Learning goal
Content
Teaching method
Content
Teaching style
Media selection
Sequence
Time constraints
Help
Presentation
Hiding
Dimming
Annotation
… to what features…Learner Preferences Usage Previous knowledge,
professional background Knowledge Interests, GoalsTask Context ComplexitySituational Context Position Setting “Ubiquitious Learning”, Learning
on demand
… and why?
Didactical reasons (Salomon 75) Preference model Compensation of deficits Reduction of deficits
Ergonomic reasons Efficiency Effectivness Acceptance
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what to what why how
adaptive sequencing 1
sequencing learning activities
tested knowledge, quiz
compensation of deficits
user tracking
adaptive sequencing 2
introduction of interaction possibilities
level of expertise usability, focus on learning activity
usage tracking
adaptive presentation
selection of media (DIVs)
preferences, learning style
compensation, acceptance
user input
adaptive navigation support
hyperlink annotation
knowledge guidance user tracking
adaptive navigation support 2
hyperlink annotation
community activities
social guidance user tracking, clustering
... ADALE 07 workshops ...
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Main Elements of IMS-LD to model Adaptivity
• Local, Global, Group, Role Properties-> Adaptation to Knowledge, Preferences, Attributes, Group, Stereotypes
• Environment->Adaptive User Interfaces and increasingly interactive learning environments
• Conditionals and Calculations->Adaptive Content Presentation
• Roles, Monitoring Services, Notification-> Collaborative Distributed Learning, Adaptability
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Three Levels of IMS LD
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what to what why how
adaptive sequencing, jazz example
predefined activity-structures
preferences, knowledge quiz
compensation of deficits activity structures, assessment LO, user dialogue
adaptive user interface, interaction facilities
introduction of environment LO, annotation possibilities, blog or wiki facilities
level of expertise, number of contributions or interactions
usability, focus on learning activity
usage tracking, calculations, properties, environments
adaptive content presentation
selection of media (DIVs)
preferences, learning style
compensation, acceptance
properties, usage tracking, condtionals, calculations
tutorial navigation support
hyperlink annotation teacher feedback, guidance local and global properties, roles, calculations
social navigation support
hyperlink annotation average learning success of peers in same activity
social guidance local and global properties, roles, calculations
synchronized collaborative learning
scaffolding activity structure
peer success in learning activities
blended collaborative learning
local, gloabl properties, conditionals
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Specht, M., Burgos, D. (2007). Modeling Adaptive Educational Methods with IMS Learning Design. Journal of Interactive Media in Education (Adaptation and IMS Learning Design. Special Issue, ed. Daniel Burgos), 2007/08. ISSN:1365-893X [jime.open.ac.uk/2007/08].
IMS LD & Adaptation
• Interface based• Learning flow based• Content based• Interactive problem solving support• Adaptive information filtering• Adaptive user grouping• Adaptive evaluation• Changes on-the-fly
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Integration of Adaptation Services in heterogenous
Environments
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CopperCore Service Integration
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Work related Scenarios
4040
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SCORM and IMS-LD
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D1.13 Current and Future Perspectives for Personalized Adaptive Learning
D1.13 Overview
• Cross-relationships, Deliverables, Events, Activities, Publications
• Major contributions given by the PROLEARN network to Personalized Adaptive Learning
• Where we stand and where we are heading in Personalized Adaptive Learning
• Sustainability
Cross-relationships with other WPs
• WP4: Interoperability and reusability, Content Federation and PAL (MACE)
• WP6: Competence-driven learning, Supporting the LLL (TenCompetence)
• WP7: Process-oriented learning, SECI Model • WP8: Survey on VCC portal (D1.8)• WP9: Summer School, Master of Active
Learning, Mini-Conference organized• WP12: Roadmap Vision Statement 1• WP15: Social SW in PAL (Journal Paper)
Some WP1 Activities• AH2004: PC Chair, Workshops• PROLEARN Workshop on Personalized Adaptive
Corporate Learning (2005)• UM2005: PROLEARN Session Personalized Adaptive
Learning on the Semantic Web• AIED05: WS on Adaptive and Adaptable Authoring• UNFOLD/PROLEARN WS on IMS Learning Design
(2005)• AH2006: organization, WS – SWEL, ADALE, A3H• ICALT2006: ADALE & AWELS WS, Keynote, Tutorial• Hypertext 2006: Adaptivity, Personalization & the
Semantic Web WS• UM2007: WS on Adaptive & Adaptable Authoring• Hypertext 2007: Practical Hypertext track
Personalization: Other Projects
• Personal Competence Manager (TENCompetence)• Contextual learning support at work (APOSDLE)• Process Oriented Learning (PROLIX)• Metadata for Content Enrichment (MACE, MELT)• Self-organized Learning (iCamp)
• Project Centred Learning (COOPER)• Semantic Web Learning Services (LUISA)• Intelligent cognitive-based open learning (iClass)• Adaptive learning spaces (ALS)
Key Associate Partners
• University of Leeds (Personalization on the Semantic Web)• Simon Fraser University Surrey (Knowledge
Representation)• University of Nottingham (Authoring of Adaptive
Hypermedia)• University Belgrade (Capturing of Learner's Feedback)• Vrije Universiteit Brussel (Adaptation Engineering)• Salzburg Research (Emotional Intelligence in Adaptation)• Athabasca University (Semantic Web in Adaptive
Education)• University of Cordoba (Personalized Recommendation)• University of Jyväskylä (Adaptation of Feedback)• Aalborg University (Semantic Web Technologies in UM)
Transfer of Tacit Knowledge
• Marcus Specht: FHG – OUNL• Lora Aroyo: TU/e – Free University Amsterdam • Geert-Jan Houben: TU/e – Free University
Brussels• Peter Dolog: L3S – Aalborg University• Alexandra Cristea: TU/e – University of Warwick• Milos Kravcik: FHG – OUNL• Daniel Burgos: OUNL – ATOS Origin
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Issues Identified
WP1 Issues and Challenges
• Standards (IMS-LD) can represent some adaptative methods, but has restrictions
• Context Dependent Instructional Designs and Reuse in Authoring
• Authoring of learning design and adaptation strategies?
• Interoperability demands – between systems & between different models/layers
• Learning standards are not harmonized – Semantic Web is used as mediator
Issues and Challenges (cont.)
• Open Corpus Adaptive Hypermedia System: operates on an open corpus of documents and LOR
• New LMS Architectures and PAL• Service Oriented Architectures and PLE• Orchestration of Services and Integration
on Social Navigation Support and Personalization
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where to go from here ?
Roadmap Vision Statement 1• Everyone (in the community of current, potential and
future knowledge workers) should be able to learn anything at anytime at anyplace
• Goals:– Provide the right learning experiences at the right time
for the target person– Everyone should have access to all public learning
materials at any time at any place• Actions:
1. Aggregation of learning resources 2. Production tools for learning resources 3. Contextual Delivery of Learning Resources – Harmonization of Learning Standards – Digital Identity Management – Business models for learning exchanges
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MACE and MELT
GRAPPLE Project• Generic Responsive Adaptive Personalized
Learning Environment (3-year STREP FP7 project initiated by PROLEARN partners)
• The WP1 deliverables D1.1/2/3/4/6/9/11 provided most of the basis for the definition of GRAPPLE
• Objective: Delivering to learners a TEL environment that guides them through a life-long learning experience, automatically adapting to personal preferences, prior knowledge, skills and competences, learning goals and the personal or social context in which the learning takes place
Sustainability/Impact
• Prolearn WP1 lives on in GRAPPLE• GRAPPLE will ensure that adaptive TEL
technology will actually be used world-wide:– integration into Moodle, Claroline and Sakai– architecture and interfaces as generic as possible to
allow easy integration into other LMSs– training, documentation and demos for authors /
educators– deployment and evaluation in higher education– deployment and evaluation in some industrial cases
Consortium
adaptive e-learning user modeling architectures
authoringmetadata
industrial learning technology
open source learning management
interaction standardslearning design
TU/e
TCD
IMC
ATOS
GILABS
OUNL
USI
VUB
LUH/L3S
UCAM
UCL
DFKI
Warwick
UniGraz
evaluation
blue: Prolearn core partner red: Prolearn associate partner
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WP1 Personalized Adaptive Learning