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

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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